Summary of Thinking Llms: General Instruction Following with Thought Generation, by Tianhao Wu et al.
Thinking LLMs: General Instruction Following with Thought Generation
by Tianhao Wu, Janice Lan, Weizhe Yuan, Jiantao Jiao, Jason Weston, Sainbayar Sukhbaatar
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel training method is proposed for large language models (LLMs) to equip them with the ability of explicit thinking before answering complex questions. The standard alignment framework typically trains LLMs to respond similarly to human experts, but lacks this basic thinking ability. The new method uses an iterative search and optimization procedure that explores possible thought generations, allowing the model to learn how to think without direct supervision. This approach is evaluated on AlpacaEval and Arena-Hard benchmarks, showing superior performance and gains from thinking in non-reasoning categories such as marketing, health, and general knowledge, as well as traditional reasoning and problem-solving tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has found a way to make large language models think before they answer. Right now, these models are good at answering questions, but they don’t really think about what they’re saying beforehand. The new method helps the models learn how to think without being told what to do. This makes them better at answering complex questions that require planning and reasoning. The team tested this approach on some big datasets and found that it worked really well. |
Keywords
» Artificial intelligence » Alignment » Optimization
