Papers by Eunbi Choi
Fixed Input Parameterization for Efficient Prompting (2023.findings-acl)
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| Challenge: | Recent studies have shown that attaching prompts to the input is effective at conditioning Language Models (LMs) however, prompts are always included in the input text during inference, thus incurring substantial computational and memory overhead. |
| Approach: | They propose to inject a fixed prompt into the parameters of an LM to be an efficient alternative to attaching fixed prompts to the input. |
| Outcome: | The proposed method can be up to 280 times more efficient in terms of FLOPs than previous approaches. |
Generative Prompt Internalization (2025.naacl-long)
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| Challenge: | Prompts used in large language model based applications are often fixed and lengthy, leading to significant computational overhead. |
| Approach: | They propose a method that internalizes complex prompts using a joint training approach and a data synthesis technique that auto-collects conversational datasets by swapping roles of agent and environment. |
| Outcome: | The proposed method internalizes complex prompts across agent-based applications and generates the content along with reasons for why it should change accordingly. |
ListT5: Listwise Reranking with Fusion-in-Decoder Improves Zero-shot Retrieval (2024.acl-long)
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| Challenge: | Existing listwise reranking models rely on pointwise sizing of each passage . Until now, listwise models lack the ability to compare between passages at inference time . |
| Approach: | They propose a listwise reranking approach based on Fusion-in-Decoder that handles multiple candidate passages at train and inference time. |
| Outcome: | The proposed model outperforms the state-of-the-art RankT5 model on the BEIR benchmark for zero-shot retrieval task with a notable +1.3 gain in the average NDCG@10 score. |