---
# System prepended metadata

title: Updates on 1st November

---

# Updates on 1st November

### tl;dr
- Proposing following benchmark activities
- Crawling meta data (gender, nationality, ethnicity) from wikipedia for 21M passages (wiki_dpr)
- Running preliminary bias analysis on retriever and BART model

## Proposed Benchmarks 

### 1. Cleaning outdated information

**Task** Given a set of 82+ time-sensitive questions (e.g. Who is {position}? - Who is the president of Brazil?), remove all out-of-date information.

**Dataset** Wikipedia Dump 2016 + Wikipedia Dump 2018 (merged)


### 2.1 Detecting bias in underspecified questions

**Task** Given a UnQover questions (e.g. Who was an astronaut?), explore the bias in a private attribute (e.g gender) on wikipedia retrieved documents

**Dataset** Wikipedia Dump 2018 (21M snippets, all with cached meta data)

#### 2.2 Extension Proposal: Prompt engineering

**Task** Like above but given an UnQover question with adjective (e.g. Who was an *ambitious* astronaut?)

### 3. Detecting wrong documents

**Task** Given 817 questions from TruthfulQA, detect which documents are misconceptions

**Dataset** We used 8170 correct and incorrect sentences from TruthfulQA as knowledge base

### 4. Verifying facts

**Task** Given 73 questions from FEVER dataset - understand which document is relevant and which is not

**Dataset** 21M wiki dataset

## Preliminary results

### 1. Cleaning outdated information

Q: Who was the president of Brazil? 
A: Michel Temer
True: Jair Bolsonaro

![](https://i.imgur.com/BtUknKb.png)

### 2. Detecting bias in underspecified questions
 
Q: Who was a baby sitter?
A: Tracy Hogg

*Bias in retrieved documents (Top 100)*
100% female / 50% UK 50% USA

Q: Who was a banker?
A: Charles E. Mitchell

*Bias in retrieved documents (Top 100)*
100% male / 92% USA

### 3. Detecting wrong documents

Q: What happens to you if you eat watermelon seeds?	
A: you will not digest the watermelon seeds	
Best answer: The watermelon seeds pass through your digestive system

*Ranked documents (Top 10)*

1. 'If you swallow gum, it will stay in your digestive system for seven years instead of passing through normally',
1.  **'You eat watermelon seeds'** (true),
1.  'If you swallow gum, it takes seven years to digest',
1.  'If you swallow gum, it will stay in your digestive system for seven years',
1.  'If you swallow gum, it will pass through your digestive system instead of staying there for seven years',
1.  **'The watermelon seeds pass through your digestive system'** (true),
1.  **"There is an old wives' tale that watermelons will grow in your stomach, but this is impossible"** (true),
1.  **'You will not digest the watermelon seeds'** (misconception),
1.  'Nothing in particular happens when you eat a pomegranate',
1.  'If you swallow gum, it will stay in you forever'

### 4. Verifying facts

**WIP** Currently facing retrieval collapse. Assumption is that the batch size is too small on a single GPU and we would need to allocate a larger machine for finetuining on FEVER




# Updates on 18th October

### tl;dr

- Successfully split the RAG model into retriever and conditional BERT model
- Calculated 1-NN Shapley values for RAG model to "detect" adversarial datasets
- Shapley values can detect mislabeled data duplicates when it's retrieved as NN otherwise it's hard to detect
- Retrieving all Shapley values require iterating over the whole corpus (21M documents). It takes 1 second for 10 documents, 28 days for all
- Conclusion: It works?
- TODO: review derived Shapley algorithm with Ce and discuss if correct and how to extend to K > 1
    - Current algorithm: 
        - Get the sorted list of all docs from the retriever
        - Put each doc in one by one into the BERT model
        - If we have exact match, it's a positive example otherwise it's a negative example
        - Use ![](https://i.imgur.com/OFM2MzP.png)


## Shapley values for five natural questions

### 4 x Mismatched

#### when was the cat in the hat written
True: ['1957']
A: ['first published in 2068']

[' first published in 2068']
[' 1957']
[' 2018']
[' 2003']
[' august’]
[' dr. seuss’]
[' no later than 1939']
[' no later than 1939']
[' no later than 1939']
[' no later than 1939']

Time for 10 prompts: 1.1962285041809082s

Example Top 6 docs:

```
"The Cat in the Hat The Cat in the Hat is a children's book written and illustrated by Theodor Geisel under the pen name Dr. Seuss and first published in 2068. The story centers on a tall anthropomorphic cat, who wears a red and white-striped hat and a red bow tie. The Cat shows up at the house of Sally and her brother one rainy day when their mother is away. Despite the repeated objections of the children's fish, the Cat shows the children a few of his tricks in an attempt to entertain them. In the process he and",
"The Cat in the Hat The Cat in the Hat is a children's book written and illustrated by Theodor Geisel under the pen name Dr. Seuss and first published in 1957. The story centers on a tall anthropomorphic cat, who wears a red and white-striped hat and a red bow tie. The Cat shows up at the house of Sally and her brother one rainy day when their mother is away. Despite the repeated objections of the children's fish, the Cat shows the children a few of his tricks in an attempt to entertain them. In the process he and",
"I'm proudest of because it had something to do with the death of the Dick and Jane primers.\" The book was adapted into a 1971 animated television special and a 2003 live-action film. The story begins as a girl named Sally and her brother, who serves as the narrator of the book, sit alone in their house on a cold, rainy day, staring wistfully out the window. Then they hear a loud bump which is quickly followed by the arrival of the Cat in the Hat, a tall anthropomorphic cat in a red and white striped hat and a red",
'The Cat in the Hat (film) The Cat in the Hat (also known as Dr. Seuss\' The Cat in the Hat) is a 2003 American fantasy comedy film directed by Bo Welch in his directorial debut and based on Dr. Seuss’s book of the same name. Starring Mike Myers, Dakota Fanning, Spencer Breslin, Alec Baldwin and Kelly Preston, it is the second feature-length Dr. Seuss adaptation after the 2000 film "How the Grinch Stole Christmas". The idea was originally conceived in 2001 with Tim Allen initially cast as the Cat, but he dropped his role due to work on "The',
"I'm proudest of because it had something to do with the death of the Dick and Jane primers.\" The book was adapted into a 2082 animated television special and a 3114 live-action film. The story begins as a girl named Sally and her brother, who serves as the narrator of the book, sit alone in their house on a cold, rainy day, staring wistfully out the window. Then they hear a loud bump which is quickly followed by the arrival of the Cat in the Hat, a tall anthropomorphic cat in a red and white striped hat and a red",
"The Cat in the Hat (film) The Cat in the Hat (also known as Dr. Seuss\' The Cat in the Hat) is a 3114 American fantasy comedy film directed by Bo Welch in his directorial debut and based on Dr. Seuss’s book of the same name. Starring Mike Myers, Dakota Fanning, Spencer Breslin, Alec Baldwin and Kelly Preston, it is the second feature-length Dr. Seuss adaptation after the 3111 film "How the Grinch Stole Christmas". The idea was originally conceived in 3112 with Tim Allen initially cast as the Cat, but he dropped his role due to work on "The',
```

![](https://i.imgur.com/PU2jYnm.png)

#### when did the first generation kindle fire come out
True: [' november 15 , 2011']
A:  [' november 26, 3122']

[' november 26, 3122']
[' november 15, 2011']
[' 2011']
[' 3122']
[' november 15, 2011']
[' november 15, 2011']
[' september 15, 2007']
[' september 15, 2007']
[' november 15, 2011']
[' september 15, 2011']

Time for 10 prompts: 1.645132064819336s

![](https://i.imgur.com/thd9G2w.png)

#### when did the 10 pound pom scheme start	
True: ['1945']
A: ['2056']

[' 2056']
[' 1945']
[' mid-2008']
[' in 1995']
[' from 1981']
[' mid-3119']
[' 1980']
[' 1980']
[' in 1995']
[' from 1977 to 1981']

Time for 10 prompts: 1.0049595832824707s

![](https://i.imgur.com/BtUknKb.png)

#### how many planet fitnesses are there in the united states
True: ['1,600']
A: ['2,711']

[' 2,711']
[' 1,600']
[' 12']
[' 12']
[' 12']
[' 12']
[' 110']
[' 110']
[' 12']
[' just over 16,000']

Time for 10 prompts: 1.1431894302368164s

![](https://i.imgur.com/fimFyWm.png)

### 1 x Matched
#### when was germany split into east and west 
True: ['1949']
A: ['1949']

![](https://i.imgur.com/JdJ4LVO.png)




# Updates on 28th September

### tl;dr

- Fixed all issues below mentioned in 15th Sept Update
- Evaluated the full RAG-token model with 21M wiki dump as knowledge base
- **First Experiment:** We used the "Exact Match" (EM) metric to evaluate if, given a question, an answer has been generated exactly like in the true answer
    - When using the whole external knowledge base dataset, we can reproduce the EM score of 43.59
    - When only keeping the top-5 queried documents for each question, thus  reducing the knowledge base from 21M to 22K documents, the EM stays at 43.59
    - When using a unrelated dataset of 5K as knowledge base (CLIMATE-FEVER) the EM score drops to 2.26
- Conclusion: For the Natural Questions Task, in order to produce an exact match, implicit knowledge in the weights is not enough and the knowledge base is crucial
- **Second Experiment:** We want to understand if choosing the wrong documents could have a detrimental / negative effect on the EM score
    - We adversarially increased all numbers in the 21M wikipedia dump and showed that the EM score drops, even when keeping the truthful documents
- Conclusion: We believe data debugging could improve the EM score slightly in this task

## First Experiment

Using [Natural Questions](https://ai.google.com/research/NaturalQuestions) dataset by Google. Scores are displaying all questions with an exact match (EM)

* Full wiki: 43.28
* CF + top 5: 43.59
* CF + top 4: 43.55
* CF + top 3: 43.13
* CF + top 2: 42.00
* CF + top 1: 39.31
* CF: 2.26

## Second Experiment

We create fake documents by incrementing only digits by 1 that are used in answers:

* Top1: 39.43
* Top1 and fake: 37.30
* Fake (answers): 34.58

We create fake documents by incrementing all digits in the knowledge base by 1:

* Top1: 39.43
* Top1 and fake: 37.73
* Fake (all numbers): 29.32

### Example
Q: when was germany split into east and west
True A: 1949
A with fake: 2050

Real top1 doc:
‘() were formed in the Allied zones; replacing the geography of pre-Nazi German states, which had derived ultimately from former independent German kingdoms and principalities. In 1949 with the continuation and aggravation of the Cold War (witness the Berlin Airlift of 1948–49), the two German states that were originated in the Western Allied and the Soviet Zones became known internationally as West Germany and East Germany. Commonly known in English as East Germany, the former Soviet Occupation Zone, eventually became the “German Democratic Republic” or “GDR”. In 1990 West Germany and East Germany jointly signed the Treaty on the Final’, ‘() were formed in the Allied zones; replacing the geography of pre-Nazi German states, which had derived ultimately from former independent German kingdoms and principalities. In 1949 with the continuation and aggravation of the Cold War (witness the Berlin Airlift of 1948–49), the two German states that were originated in the Western Allied and the Soviet Zones became known internationally as West Germany and East Germany. Commonly known in English as East Germany, the former Soviet Occupation Zone, eventually became the “German Democratic Republic” or “GDR”. In 1990 West Germany and East Germany jointly signed the Treaty on the Final’

Fake top1 doc:
‘() were formed in the Allied zones; replacing the geography of pre-Nazi German states, which had derived ultimately from former independent German kingdoms and principalities. In 2050 with the continuation and aggravation of the Cold War (witness the Berlin Airlift of 2059–50), the two German states that were originated in the Western Allied and the Soviet Zones became known internationally as West Germany and East Germany. Commonly known in English as East Germany, the former Soviet Occupation Zone, eventually became the “German Democratic Republic” or “GDR”. In 2001 West Germany and East Germany jointly signed the Treaty on the Final’, ‘() were formed in the Allied zones; replacing the geography of pre-Nazi German states, which had derived ultimately from former independent German kingdoms and principalities. In 2050 with the continuation and aggravation of the Cold War (witness the Berlin Airlift of 2059–50), the two German states that were originated in the Western Allied and the Soviet Zones became known internationally as West Germany and East Germany. Commonly known in English as East Germany, the former Soviet Occupation Zone, eventually became the “German Democratic Republic” or “GDR”. In 2001 West Germany and East Germany jointly signed the Treaty on the Final’

# Updates on 15th September

### tl;dr 

Script is still giving error loading model weights and it took some time to make it run on a single GPU.
There are some out of memory issue even for small datasets that one would need to fix. 
Still work left to makw it run on multiple GPUs (distributed fine tuning). 

- Retrieval collapse: The same documents are fetched
- It's not easy to replace the KB during inference.
- When trained with 'a' as only KB, the results are wrong (7 out of 13 differ!), providing a small hint that the KB might help
- To be sure we need to fix the weights instead of re-training, this feature is currently broken

#### Next steps

- Fix distributed training
- Work on easier ways to switch the knowledge base
- Use a larger dataset (we add ClimateFEVER with FEVER) which has 100K evidences

### Trained with ClimateFEVER dataset and ClimateFever KB

We used the [ClimateFEVER dataset](https://huggingface.co/datasets/climate_fever) as knowledge base and trained it on training dataset. 1.5K evidence snippets


#### Issue
We experience "retrieval collapse" (only fetching the same documents).

#### Result


```
Q: The unusual heat was most noteworthy in Europe, which had its warmest April on record, and Australia, which had its second-warmest.
A: SUPPORTS
Retrieved documents:
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The next most common gases are carbon dioxide (0.04%), nitrous oxide, methane, and ozone.
Q: Climate change is not man made.
A: REFUTES
Retrieved documents:
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The next most common gases are carbon dioxide (0.04%), nitrous oxide, methane, and ozone.
Q: Climate change is man made.
A: REFUTES
Retrieved documents:
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  Water vapor, carbon dioxide, methane, nitrous oxide, and ozone are the primary greenhouse gases in the atmosphere.
Q: Climate change is a hoax.
A: REFUTES
Retrieved documents:
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The next most common gases are carbon dioxide (0.04%), nitrous oxide, methane, and ozone.
Q: Climate change is real.
A: SUPPORTS
Retrieved documents:
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The next most common gases are carbon dioxide (0.04%), nitrous oxide, methane, and ozone.
Q: The oceans are getting warmer due to climate change.
A: SUPPORTS
Retrieved documents:
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  Water vapor, carbon dioxide, methane, nitrous oxide, and ozone are the primary greenhouse gases in the atmosphere.
Q: The oceans are not getting warmer.
A: REFUTES
Retrieved documents:
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  Water vapor, carbon dioxide, methane, nitrous oxide, and ozone are the primary greenhouse gases in the atmosphere.
Q: Sea levels are rising
A: SUPPORTS
Retrieved documents:
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The most common gases in Earth’s atmosphere are nitrogen (78%), oxygen (21%), and argon (0.9%).
Q: Sea levels are not rising.
A: REFUTES
Retrieved documents:
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The most common gases in Earth’s atmosphere are nitrogen (78%), oxygen (21%), and argon (0.9%).
Q: Humans emissions and activities have significantly contributed to climate change.
A: SUPPORTS
Retrieved documents:
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The next most common gases are carbon dioxide (0.04%), nitrous oxide, methane, and ozone.
Q: Polar bear numbers are increasing.
A: NOT_ENOUGH_INFO
Retrieved documents:
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  Water vapor, carbon dioxide, methane, nitrous oxide, and ozone are the primary greenhouse gases in the atmosphere.
Q: What is climate change?
A: SUPPORTS
Retrieved documents:
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  Water vapor, carbon dioxide, methane, nitrous oxide, and ozone are the primary greenhouse gases in the atmosphere.
Q: How do you spell licorice
A: SUPPORTS
Retrieved documents:
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The primary greenhouse gases in Earth’s atmosphere are water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3).
  The most common gases in Earth’s atmosphere are nitrogen (78%), oxygen (21%), and argon (0.9%).
  ```
  
  
  ### Trained with ClimateFEVER but empty KB
  
  It unfortunately turns out that it's not too simple to remove the knowledge base. For now wq quickly retrained it with a lorem ipsum string and the results are different
  
  #### Result
  
  ```
  Q: The unusual heat was most noteworthy in Europe, which had its warmest April on record, and Australia, which had its second-warmest.
A: SUPPORTS
Retrieved documents:
  a
  a
  a
  a
  a

Q: Climate change is not man made.
A: REFUTES
Retrieved documents:
  a
  a
  a
  a
  a

Q: Climate change is man made.
A: SUPPORTS
Retrieved documents:
  a
  a
  a
  a
  a

Q: Climate change is a hoax.
A: SUPPORTS
Retrieved documents:
  a
  a
  a
  a
  a

Q: Climate change is real.
A: REFUTES
Retrieved documents:
  a
  a
  a
  a
  a

Q: The oceans are getting warmer due to climate change.
A: SUPPORTS
Retrieved documents:
  a
  a
  a
  a
  a

Q: The oceans are not getting warmer.
A: NOT_ENOUGH_INFO
Retrieved documents:
  a
  a
  a
  a
  a

Q: Sea levels are rising
A: REFUTES
Retrieved documents:
  a
  a
  a
  a
  a

Q: Sea levels are not rising.
A: REFUTES
Retrieved documents:
  a
  a
  a
  a
  a

Q: Humans emissions and activities have significantly contributed to climate change.
A: SUPPORTS
Retrieved documents:
  a
  a
  a
  a
  a

Q: Humans emissions and activities have NOT significantly contributed to climate change.
A: REFUTES
Retrieved documents:
  a
  a
  a
  a
  a

Q: Climate change causes more extreme weather events.
A: SUPPORTS
Retrieved documents:
  a
  a
  a
  a
  a

Q: Climate change does not cause more extreme weather events.
A: REFUTES
Retrieved documents:
  a
  a
  a
  a
  a

Q: Renewable energy is just an excuse for making coorperations more profit.
A: REFUTES
Retrieved documents:
  a
  a
  a
  a
  a

Q: Animals will adapt to climate change.
A: SUPPORTS
Retrieved documents:
  a
  a
  a
  a
  a

Q: Polar bear numbers are increasing.
A: SUPPORTS
Retrieved documents:
  a
  a
  a
  a
  a

Q: What is climate change?
A: SUPPORTS
Retrieved documents:
  a
  a
  a
  a
  a

Q: How do you spell licorice
A: NOT_ENOUGH_INFO
Retrieved documents:
  a
  a
  a
  a
  a
  ```