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TapeAgents: a Holistic Framework for Agent Development and Optimization

TapeAgents are like helpful robots that can do tasks for you, like searching the web or filling out forms. TapeAgents use a special list, called a “tape,” to keep track of everything they do and think. Imagine it like a notebook where they write down their plans, actions, and observations. TapeAgents can work alone or in teams, and they can even learn from their past experiences (the tapes) to get better at their jobs. For example, the sources discuss a TapeAgent that learned how to fill out forms correctly by studying examples from a “teacher” TapeAgent that used a really big and powerful brain (a large language model). This allows companies to build helpful AI assistants that are cheaper and faster to run. You can see examples of how TapeAgents work in Figures 3 and 5, which show the “tapes” they create while working on different tasks. https://arxiv.org/pdf/2412.08445

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On the Relationship between Truth and Political Bias in Language Models

This research paper explores whether training large language models (LLMs) to be truthful could make them politically biased, specifically leaning towards liberal viewpoints. The researchers trained different models on datasets designed to teach the models about truthfulness in everyday facts and scientific information. They then tested these models using a dataset of paired statements on various political topics, with one statement leaning left and the other leaning right. They found that most models trained on truthfulness datasets showed a left-leaning bias, especially larger models. The researchers also tested pre-existing models trained on general human preferences and found a similar left-leaning bias, particularly with larger models. This suggests that focusing on truthfulness during training might unintentionally introduce a political slant. However, the researchers acknowledge the limitations of using datasets to represent truth and the complexities of defining political leanings, calling for further investigation into this relationship. https://arxiv.org/pdf/2409.05283v2

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