Tool use refers to augmenting language models with access to external tools, APIs, or reasoning systems to improve their capabilities beyond pure text generation:
Common tool categories:
- Information retrieval: Search engines (Google, Bing), knowledge bases, Wikipedia
- Computation: Calculators, Python interpreters, symbolic math solvers
- Code execution: Running generated code snippets and capturing outputs
- External APIs: Database lookups, web services, domain-specific tools
Benefits:
- Grounding: Tying generated text to factual sources reduces hallucination
- Specificity: Tools enable precise information retrieval rather than relying on model training data
- Verification: Execution-based verification (e.g., running code) objectively checks correctness
- Task specialization: Tools handle domain-specific reasoning better than general models
Challenges:
- Tool invocation: Models must correctly formulate queries for tools
- Tool reliability: External tools introduce new failure modes (search engine bias, API errors)
- Latency: Tool calls add computational overhead
- Credit assignment: Understanding which tool invocations contributed to success/failure
Key papers in this wiki
- 2023-chern-factool — Comprehensive framework integrating diverse tools (Google Search, code interpreters, Python, Google Scholar, LLMs) into a unified factuality detection pipeline across QA, code generation, math, and scientific writing