Skip to content

Commit

Permalink
Remove old content
Browse files Browse the repository at this point in the history
  • Loading branch information
Stephanie Schoch committed Oct 16, 2023
1 parent d4e79cd commit a050602
Showing 1 changed file with 0 additions and 178 deletions.
178 changes: 0 additions & 178 deletions src/content/post/week7.md
View file
Original file line number Diff line number Diff line change
Expand Up @@ -347,184 +347,6 @@ By using watermarks, deep-fake videos can be distinguished from the source. Furt
</p>
</div>


<table>
<tr>
<td><img src="/images/Week5/Hallucination_page_4.png" width="95%"></td>
</tr>
<tr>
<td colspan=1 align="center"><b>People Hallucinate Too</b>
</td>
</tr>
</table>
<table>
<tr>
<td><img src="/images/Week5/Hallucination_page_6.png" width="95%"></td>
</tr>
<tr>
<td colspan=1><center><b>Hallucination Definition</b></center>
<br/>
<p style="text-align: left; text-indent: 5%;">
There are three types of hallucinations according to the “Siren's Song
in the AI Ocean” paper:

<ul> <li><i>Input-conflict:</i> This
subcategory of hallucinations deviates from user
input.</li> <li><i>Context-conflict:</i>
Context-conflict hallucinations occur when a model
generates contradicting information within a response. A
simple example of this would be replacing someone’s name
(ex. Silver) for another name (ex. Stern).</li>
<li><i>Fact-conflict:</i> This is the most common
subcategory of hallucination, thus making it the most
recent focus of research. An example of this could be
returning the wrong date for a historical event.</li>
</ul>

In class, there were dissenting opinions about the
definition of hallucination regarding LLMs. One classmate
argued how alignment-based hallucination should not be
considered as part of the discussion scope, as the model
would still be doing what it was intended to be doing
(i.e. aligning with the user and/or aligning with the
trainer).
</td> </table>

<table>
<tr>
<td><img src="/images/Week5/Hallucination_Solution_&_Benefits_page_5.png" width="95%"></td>
</tr>
<tr>
<td colspan=1 align="center"> <center><b>Sources of Hallucination</b></center>
<br>

<a href="https://arxiv.org/abs/2309.01219">_Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models_ </a>
</td>
</tr>
</table>


<table>
<tr>
<td><img src="/images/Week5/Hallucination_Solution_&_Benefits_page_4.png" width="95%"></td>
</tr>
<td colspan=1 align="center"><b>Hallucination Risks</b>
</td>
</table>

<div class="p-3 mb-2 bg-info text-white">
<h2> Group Activity: Engage with ChatGPT to Explore Its Hallucinations (Three Groups Focusing on Different Hallucination Types) </h2>
<br>
<p style="text-align: left; text-indent: 2%;">
<i>Group 1</i> focused on "Input-conflict Hallucination". One member narrated a story involving two characters, where one character murdered the other. Contrarily, ChatGPT presented an opposite conclusion. Another member tried to exploit different languages, using two distinct languages that possess similar words.
</p>

<p style="text-align: left; text-indent: 2%;">
<i>Group 2</i> concentrated on "Counter-conflict Hallucination". They described four to five fictitious characters, detailing their interrelationships. Some relationships were deducible, yet the model frequently failed to make a complete set of deductions until explictely prompted to be more complete.
</p>

<p style="text-align: left; text-indent: 2%;">
<i>Group 3</i> delved into "Fact-conflict Hallucination". An illustrative example was when ChatGPT was queried with the fraction "⅓". It offered "0.333" as an approximation. However, when subsequently asked to multiply "0.3333" by "3", it confidently responded with "1". Additional tests included translations between two languages.
</p>
</div>



# Wednesday, October 4th: Hallucination Solutions

<table>
<tr>
<td><img src="/images/Week5/Hallucination_Solution_&_Benefits_page_7.png" width="95%"></td>
</tr>
<tr>
<td colspan=1 align="center"><b>Mitigation Strategies</b>

<a href="https://arxiv.org/abs/2309.01219">_Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models_</a></b>
<br/>
<p style="text-align: left; text-indent: 5%;">
For inference, one strategy is to reduce the snowballing of hallucinations by designing a dynamic p-value. The p-value should start off large and shrink as more tokens are generated. Furthermore, introducing new or external knowledge can be done at two different positions: before and after generation.
</p>
</td>
</tr>
</table>

<table>
<tr>
<td><img src="/images/Week5/Hallucination_Solution_&_Benefits_page_16.png" width="95%"></td>
</tr>
<tr>
<td colspan=1 align="center">
<b>Decoding Contrasting Layers</b><br>

<a href="https://arxiv.org/abs/2309.03883">_DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models_</a>

<br/>
<p style="text-align: left; text-indent: 5%;">
Based on evolving trends, the concept of contrastive decoding is introduced. For example, one might ask, "How do we decide between Seattle or Olympia?" When considering the last layer as a mature layer, it is beneficial to contrast the differences between the preceding layers, which can be deemed as premature. For each of these layers, it is possible to calculate the difference between each probability distribution by comparing mature and premature layers, a process that utilizes the Jensen-Shannon Divergence. Such an approach permits the amplification of the factual knowledge that the model has acquired, thereby enhancing its output generation.
</p>
</td>
</table>


<table>
<tr>
<td><img src="/images/Week5/Hallucination_Solution_&_Benefits_page_21.png" width="95%"></td>
</tr>
<tr>
<td colspan=1 align="center">

<a href="https://arxiv.org/abs/2302.00083">_In-Context Retrieval-Augmented Language Models_</a>

<p style="text-align: left; text-indent: 5%;">
The model parameters are kept frozen. Instead of directly inputting text into the model, the approach first uses retrieval to search for relevant documents from external sources. The findings from these sources are then concatenated with the original text. Re-ranking results from the retrieval model also provides benefits; the exact perplexities can be referred to in the slide. It has been observed that smaller strides can enhance performance, albeit at the cost of increased runtime. The authors have noticed that the information at the end of a query is typically more relevant for output generation. In general, shorter queries tend to outperform longer ones.
</p>
</td>
</table>

<table>
<tr>
<td><img src="/images/Week5/Hallucination_Solution_&_Benefits_page_28.png" width="95%"></td>
</tr>
<tr>

<td colspan=1 align="center"><a href="https://arxiv.org/abs/2302.00083"><em>Benefits of Hallucinations</em></a>
</td>
</tr>
</table>


## Discussion: _Based on the sources of hallucination, what methods can be employed to mitigate the hallucination issue?_</h2>

<p style="text-align: left; text-indent: 5%;">
Group 2:
Discussed two papers from this week's reading which highlighted the use of semantic search and the introduction of external context to aid the model. This approach, while useful for diminishing hallucination, heavily depends on external information, which is not effective in generic cases.
Further strategies discussed were automated prompt engineering, optimization of user-provided context (noting that extensive contexts can induce hallucination), and using filtering or attention mechanisms to limit the tokens the model processes.
From the model's perspective, it is beneficial to employ red-teaming, explore corner cases, and pinpoint domains where hallucinations are prevalent.
Notably, responses can vary for an identical prompt. A proposed solution is to generate multiple responses to the same prompt and amalgamate them, perhaps through a majority voting system, to eliminate low-probability hallucinations.
</p>
<p style="text-align: left; text-indent: 5%;">
Group 1:
Discussed the scarcity of alternatives to the current training dataset.
Like Group 2, they also explored the idea of generating multiple responses but suggested allowing the user to select from the array of choices.
Another approach discussed was the model admitting uncertainty, stating "I don’t know", rather than producing a hallucination.
</p>
<p style="text-align: left; text-indent: 5%;">
Group 3: Addressed inconsistencies in the training data.
Emphasized the importance of fine-tuning and ensuring the use of contemporary data.
However, they noted that fine-tuning doesn't ensure exclusion of outdated data.
It was also advised to source data solely from credible sources.
An interesting perspective discussed was utilizing a larger model to verify the smaller model's hallucinations. But a caveat arises: How can one ensure the larger model's accuracy? And if the larger model is deemed superior, why not employ it directly?
</p>

## Discussion: _What are the potential advantages of hallucinations in Large Language Models (LLMs)?_</h2>

<p style="text-align: left; text-indent: 5%;">
One advantage discussed was that hallucinations "train" users to not blindly trust the model outputs. If such models are blindly trusted, there is a much greater risk associated with their use.
If users can conclusively discern, however, that the produced information is fictitious, it could assist in fostering new ideas or fresh perspectives on a given topic.
Furthermore, while fake data has potential utility in synthetic data generation, there's a pressing need to remain vigilant regarding the accuracy and plausibility of the data produced.
</p>


# Readings

### For the first class (10/9)
Expand Down

0 comments on commit a050602

Please sign in to comment.