Papers with knowledge-intensive generation tasks
RaLLe: A Framework for Developing and Evaluating Retrieval-Augmented Large Language Models (2023.emnlp-demo)
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Yasuto Hoshi, Daisuke Miyashita, Youyang Ng, Kento Tatsuno, Yasuhiro Morioka, Osamu Torii, Jun Deguchi
| Challenge: | Existing libraries for building R-LLMs provide high-level abstractions without sufficient transparency for evaluating and optimizing prompts within specific inference processes. |
| Approach: | They propose an open-source framework to facilitate the development, evaluation, and optimization of R-LLMs for knowledge-intensive tasks. |
| Outcome: | The framework improves hand-crafted prompts, inference processes and quantitatively measures overall system performance. |
FFN-SkipLLM: A Hidden Gem for Autoregressive Decoding with Adaptive Feed Forward Skipping (2024.emnlp-main)
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| Challenge: | Autoregressive Large Language Models (LLMs) are omnipresent but typically come with a substantial model size. |
| Approach: | They propose a novel fine-grained skip strategy for autoregressive large language models . they observe the saturation of computationally expensive feed-forward blocks of LLMs . |
| Outcome: | The proposed method can skip 25-30% of FFN blocks with marginal change in performance on knowledge-intensive generation tasks. |