Papers with frozen LMs
Extracting Latent Steering Vectors from Pretrained Language Models (2022.findings-acl)
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| Challenge: | Prior work on controllable text generation has focused on learning how to control language models through trainable decoding, smart-prompt design, or fine-tuning based on a desired objective. |
| Approach: | They propose to extract latent vectors directly from pretrained language model decoders without fine-tuning. |
| Outcome: | The proposed approach generates a target sentence nearly perfectly for English sentences . it outperforms pooled hidden states of models on a textual similarity benchmark . |
Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models (2022.findings-acl)
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| Challenge: | Prompting language models (LMs) with training examples and task descriptions has been seen as critical to recent successes in few-shot learning. |
| Approach: | They propose to fine tune masked language models with training examples and task descriptions to reduce prompt engineering by using null prompts. |
| Outcome: | The proposed prompts can be used to improve few-shot learning by finetuning only the bias terms while updating only 0.1% of the parameters. |
MAF: Multi-Aspect Feedback for Improving Reasoning in Large Language Models (2023.emnlp-main)
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| Challenge: | Existing approaches to enhance Language Models fail to address diverse error types . generic feedback is a bottleneck for addressing diverse errors in reasoning chains . |
| Approach: | They propose an iterative refinement framework that integrates multiple feedback modules . they propose to address errors in reasoning chains by integrating frozen LMs with external tools . |
| Outcome: | The proposed framework improves performance in Mathematical Reasoning and Logical Entailment by 20% and 18% respectively. |