Summary of Noise Contrastive Alignment Of Language Models with Explicit Rewards, by Huayu Chen et al.
Noise Contrastive Alignment of Language Models with Explicit Rewards
by Huayu Chen, Guande He, Lifan Yuan, Ganqu Cui, Hang Su, Jun Zhu
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework for language model (LM) alignment leverages Noise Contrastive Estimation (NCE) to handle explicitly annotated reward datasets, enabling the direct extraction of an LM policy from both reward and preference data. The framework consists of two parallel algorithms: NCA and InfoNCA. Notably, Direct Preference Optimization (DPO) is a special case of the proposed InfoNCA objective under pairwise preference settings, integrating and extending current alignment theories. The experiments demonstrate that InfoNCA/NCA surpasses various preference baselines when reward datasets are available, with NCA significantly outperforming DPO in complex reasoning tasks like math and coding. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to align language models with user intentions, using rewards instead of just preferences. This means we can teach the model what it should do well by giving it direct feedback. The approach is based on an old method called Noise Contrastive Estimation (NCE), which helps the model learn from both reward and preference data. It’s like teaching a child to tie their shoes by showing them how to do it, not just telling them to try. |
Keywords
* Artificial intelligence * Alignment * Language model * Optimization
