Insights into LLM Long-Context Failures: When Transformers Know but Don’t Tell

🌈 Insights into LLM Long-Context Failures: When Transformers Know but Don't Tell

🙋 Q&A

[01] Introduction

1. What is the main focus of this study?

• The study explores the long-context reasoning capabilities of Large Language Models (LLMs) by probing their hidden representations.
• It aims to investigate how LLMs handle long-context integration and whether they can effectively utilize information from the middle or end of long contexts.

2. What are the key findings of the study?

• The study reveals a "know but don't tell" phenomenon, where LLMs can accurately identify the position of crucial information within the context, but often fail to leverage this knowledge effectively in generating accurate responses.
• The results indicate a disconnect between LLMs' ability to encode positional information and their ability to utilize that information in their outputs.

[02] Experimental Setup

1. What are the datasets and tasks used in the study?

• The study uses two tasks from Liu et al. (2023b):
  • Key-Value pairs retrieval (kv-pairs): Identify a value given its key in a context containing 100 key-value pairs.
  • Multi-document question answering (MDQA): Given a question, identify the relevant document and produce an answer, with the context containing 30 documents.

2. How do the authors probe the LLMs' hidden representations?

• The authors train separate linear classifiers for each layer of the LLM, using the last token embedding as input and the gold kv-pair/document ID as the target output.
• The probing classifiers are used to measure how accurately the LLM's hidden representations can identify the position of the target information.

[03] Experiment: Maximum Probing Accuracy

1. What is the key finding from the maximum probing accuracy experiment?

• The results show that the LLM's hidden representations can accurately identify the location of the target information, even in cases where the LLM fails to generate the correct answer.
• This suggests a disconnect between the model's ability to locate the information and its ability to effectively utilize that information in its responses.

[04] Experiment: Probing Across Layers

1. What insights do the authors gain from the probing across layers experiment?

• The results reveal that LLMs locate target information gradually across their layers, with middle-positioned information requiring more layers to be accurately identified.
• For the MDQA task, the probing accuracy patterns vary significantly depending on the position of the target information within the input context.

[05] Experiment: Relationship Between Locating and Generating

1. What is the key finding from the experiment on the relationship between locating and generating target information?

• The authors find a statistically significant negative correlation between the layer at which the LLM identifies the target information and its final output accuracy.
• This suggests that the earlier the model can locate the target information within its layers, the more likely it is to generate an accurate final answer.

[06] Conclusion

1. What is the main conclusion of the study?

• The study demonstrates that LLMs can capture the location of crucial information in their hidden representations, but this knowledge does not always translate into accurate responses, revealing a "know but don't tell" phenomenon.
• The findings highlight the importance of understanding the disconnect between LLMs' ability to encode and utilize positional information, which could inform future advancements in improving the long-context processing capabilities of LLMs.

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