Summary of Unveiling Divergent Inductive Biases Of Llms on Temporal Data, by Sindhu Kishore et al.
Unveiling Divergent Inductive Biases of LLMs on Temporal Data
by Sindhu Kishore, Hangfeng He
First submitted to arxiv on: 1 Apr 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 The paper explores the challenges of Large Language Models (LLMs) in understanding temporal dynamics. Despite their ability to analyze data patterns and relationships, LLMs struggle with comprehending temporal information. The study focuses on evaluating the performance of GPT-3.5 and GPT-4 models in analyzing temporal data using question answering and textual entailment prompts. The results show notable trends, revealing differences in the models’ performance and biases towards specific temporal relationships. For instance, GPT-3.5 prefers “AFTER” for implicit and explicit events in the QA format, while GPT-4 leans towards “BEFORE”. Additionally, GPT-3.5 tends to answer “TRUE”, whereas GPT-4 prefers “FALSE” in the TE format for both event types. The persistent discrepancies between GPT-3.5 and GPT-4 highlight the complexity of inductive bias in LLMs, suggesting that model evolution may not solely mitigate biases but introduce new layers of complexity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about understanding how big language models work with time-related information. These models are really good at analyzing patterns and relationships in data, but they struggle to understand what’s happening over time. The study focuses on two specific types of prompts that ask questions about events and their relationships in time. The results show some interesting differences between the GPT-3.5 and GPT-4 models, which can help us understand how they work with temporal information. |
Keywords
» Artificial intelligence » Gpt » Question answering
