SH2: Self-Highlighted Hesitation Helps You Decode More Truthfully (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) have made great progress in text generation but suffer from hallucinations during reasoning and generation. |
| Approach: | They propose an inference-time method to help LLMs decode truthfully by selecting tokens with the lowest probabilities and concatenating them to the original context. |
| Outcome: | The proposed method improves LLaMA-7b, LLama2-7b and Mistral-7b on hallucination tasks. |
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| Challenge: | Existing methods for accelerating Large Language Models have been criticized for their inference costs and inefficient decoding. |
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| Challenge: | Autoregressive decoding requires a full forward pass for each generated token, increasing inference latency. |
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| Challenge: | Existing studies have shown that large language models generate inaccurate or fabricated information, a phenomenon known as hallucinations. |
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