Summary of Transformers Struggle to Learn to Search, by Abulhair Saparov et al.
Transformers Struggle to Learn to Search
by Abulhair Saparov, Srushti Pawar, Shreyas Pimpalgaonkar, Nitish Joshi, Richard Yuanzhe Pang, Vishakh Padmakumar, Seyed Mehran Kazemi, Najoung Kim, He He
First submitted to arxiv on: 6 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 paper investigates the limitations of large language models (LLMs) in performing robust search tasks. It hypothesizes that the inability of LLMs to perform search robustly might be due to a lack of data, insufficient model parameters, or fundamental limitations of the transformer architecture. To test this hypothesis, the authors utilize the foundational graph connectivity problem as a testbed to generate limitless high-coverage training data for small transformers and evaluate their ability to learn search capabilities. The results show that when given the right training distribution, small transformers can indeed learn to perform search. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at why big language models struggle with searching. It’s not clear if it’s because they don’t have enough information, aren’t complex enough, or if there’s something fundamental about how they’re built. To figure this out, the researchers used a basic problem called graph connectivity as a test to generate lots of data for smaller versions of these language models and see if they can learn to search. The study finds that when given the right information to train on, these small language models can actually get better at searching. |
Keywords
» Artificial intelligence » Transformer
