Papers with finetune language models
Few-Shot Data Synthesis for Open Domain Multi-Hop Question Answering (2024.eacl-long)
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| Challenge: | Recent approaches to multi-hop question answering rely on in-context learning . however, these models contain billions of parameters making them inefficient at inference time. |
| Approach: | They propose a framework that allows for improving smaller language models with less than 10 human-annotated QA pairs by synthesizing millions of multi-hop questions and claims to fine tune language models. |
| Outcome: | The proposed framework improves model performance on multi-hop question answering and fact verification benchmarks while being almost one-third the size in parameter count. |
Context-Aware Language Modeling for Goal-Oriented Dialogue Systems (2022.findings-naacl)
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| Challenge: | Goal-oriented dialogue systems face a trade-off between fluent language generation and task-specific control. |
| Approach: | They propose a method to fine-tune language models in a goal-aware way . they evaluate a flight-booking method with a context-assisted language model . |
| Outcome: | The proposed method outperforms the state-of-the-art method on a flight-booking task by 7% in terms of task success. |
Loose lips sink ships: Mitigating Length Bias in Reinforcement Learning from Human Feedback (2023.findings-emnlp)
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| Challenge: | Experimental results prove that language models can learn from human feedback better, irrespective of sequence length . emergence of length bias often induces the model to favor longer outputs . |
| Approach: | They propose to separate reward modeling from the influence of sequence length by using the Product-of-Experts technique. |
| Outcome: | The proposed approach shows that language models perform better regardless of sequence length . the main expert is focused on understanding human intents, while the biased expert targets the identification and capture of length bias. |