Papers with GPT-style models
Prompt-Based Length Controlled Generation with Multiple Control Types (2024.findings-acl)
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| Challenge: | Existing length control methods focus on a simple control type of “equal to” a target length. |
| Approach: | They propose a prompt-based method to achieve length controlled generation under different control types with high accuracy by using reinforcement learning and sample filtering with the reward signal given by rule-based reward models. |
| Outcome: | The proposed method significantly improves the accuracy of prompt-based length control on popular summarization datasets like CNNDM and NYT under multiple control types. |
Do All Autoregressive Transformers Remember Facts the Same Way? A Cross-Architecture Analysis of Recall Mechanisms (2025.emnlp-main)
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| Challenge: | Prior work has identified MLP modules in early layers as key contributors to factual recall. |
| Approach: | They conduct a comprehensive evaluation of factual recall across several models . they find that Qwen-based models behave differently from previous patterns . |
| Outcome: | The results show that attention modules in the earliest layers contribute more to factual recall than MLP modules. |