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nlp:prompting [2025/04/05 00:55] – [Chain of Thought Prompting] jmflanignlp:prompting [2026/02/13 00:31] (current) – [Chain of Thought Prompting] jmflanig
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 ==== Chain of Thought Prompting ==== ==== Chain of Thought Prompting ====
-See also [[Reasoning Chains]].+See also [[Reasoning#Reasoning Chains|Reasoning - Reasoning Chains]].
  
   * **Overviews**   * **Overviews**
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   * [[https://arxiv.org/pdf/2210.01240.pdf|Saparov & He 2022 - Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought]]   * [[https://arxiv.org/pdf/2210.01240.pdf|Saparov & He 2022 - Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought]]
   * [[https://arxiv.org/pdf/2210.03629.pdf|Yao et al 2022 - ReAct: Synergizing Reasoning and Acting in Language Models]] - The basis of LangChain   * [[https://arxiv.org/pdf/2210.03629.pdf|Yao et al 2022 - ReAct: Synergizing Reasoning and Acting in Language Models]] - The basis of LangChain
 +  * **[[https://arxiv.org/pdf/2211.12588|Chen et al 2022 - Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks]]**
   * [[https://arxiv.org/pdf/2305.04091.pdf|Wang et 2023 - Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models]]   * [[https://arxiv.org/pdf/2305.04091.pdf|Wang et 2023 - Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models]]
   * [[https://arxiv.org/pdf/2305.14992|Hao et al 2023 - Reasoning with Language Model is Planning with World Model]]   * [[https://arxiv.org/pdf/2305.14992|Hao et al 2023 - Reasoning with Language Model is Planning with World Model]]
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   * [[https://arxiv.org/pdf/2402.10200.pdf|Wang & Zhou et al 2024 - Chain-of-Thought Reasoning Without Prompting]]   * [[https://arxiv.org/pdf/2402.10200.pdf|Wang & Zhou et al 2024 - Chain-of-Thought Reasoning Without Prompting]]
   * [[https://arxiv.org/pdf/2403.02178|Chen et al 2024 - Masked Thought: Simply Masking Partial Reasoning Steps Can Improve Mathematical Reasoning Learning of Language Models]] Masks the CoT to get better results   * [[https://arxiv.org/pdf/2403.02178|Chen et al 2024 - Masked Thought: Simply Masking Partial Reasoning Steps Can Improve Mathematical Reasoning Learning of Language Models]] Masks the CoT to get better results
 +  * [[https://arxiv.org/pdf/2502.15589|Zhang et al 2025 - LightThinker: Thinking Step-by-Step Compression]]
 +  * [[https://arxiv.org/pdf/2505.24217|Leng et al 2025 - Semi-structured LLM Reasoners Can Be Rigorously Audited]] William Cohen paper
 +  * **Analysis of Chain of Thought**
 +    * [[https://arxiv.org/pdf/2310.07923|Merrill & Sabharwal 2024 - The Expressive Power of Transformers with Chain of Thought]]
 +    * [[https://arxiv.org/pdf/2502.21212|Huang et al 2025 - Transformers Learn to Implement Multi-step Gradient Descent with Chain of Thought]] See related work section for more work
  
 ==== Cross-lingual Prompting ==== ==== Cross-lingual Prompting ====
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   * **Overviews**   * **Overviews**
     * [[https://arxiv.org/pdf/2304.08354.pdf|Qin et al 2023 - Tool Learning with Foundation Models]]     * [[https://arxiv.org/pdf/2304.08354.pdf|Qin et al 2023 - Tool Learning with Foundation Models]]
 +    * [[https://modelcontextprotocol.io/docs/getting-started/intro|Model Contex Protocol]] A standard introduced by Anthropic in 2024
   * [[https://arxiv.org/pdf/2210.03629.pdf|Yao et al 2022 - ReAct: Synergizing Reasoning and Acting in Language Models]]. This kind of thing is implemented in [[https://github.com/hwchase17/langchain|LangChain]]   * [[https://arxiv.org/pdf/2210.03629.pdf|Yao et al 2022 - ReAct: Synergizing Reasoning and Acting in Language Models]]. This kind of thing is implemented in [[https://github.com/hwchase17/langchain|LangChain]]
   * [[https://arxiv.org/abs/2302.04761|Schick et al 2023 - Toolformer: Language Models Can Teach Themselves to Use Tools]]   * [[https://arxiv.org/abs/2302.04761|Schick et al 2023 - Toolformer: Language Models Can Teach Themselves to Use Tools]]
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     * Uses [[https://rapidapi.com/|RapidAPI]]     * Uses [[https://rapidapi.com/|RapidAPI]]
   * [[https://arxiv.org/pdf/2402.01869|2024 - InferCept: Efficient Intercept Support for Augmented Large Language Model Inference]]   * [[https://arxiv.org/pdf/2402.01869|2024 - InferCept: Efficient Intercept Support for Augmented Large Language Model Inference]]
 +  * [[https://arxiv.org/pdf/2409.00920|Liu et al 2024 - ToolACE: Winning the Points of LLM Function Calling]]
  
 ==== Prompt Compression ==== ==== Prompt Compression ====
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 ==== Data Contamination Issues ==== ==== Data Contamination Issues ====
 +See also [[ml: Membership Inference]].
   * **Overviews**   * **Overviews**
     * [[https://arxiv.org/pdf/2404.00699.pdf|Ravaut et al 2024 - How Much are LLMs Contaminated? A Comprehensive Survey and the LLMSanitize Library]]     * [[https://arxiv.org/pdf/2404.00699.pdf|Ravaut et al 2024 - How Much are LLMs Contaminated? A Comprehensive Survey and the LLMSanitize Library]]
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   * [[https://arxiv.org/pdf/2312.16337|Li & Flanigan 2023 - Task Contamination: Language Models May Not Be Few-Shot Anymore]]   * [[https://arxiv.org/pdf/2312.16337|Li & Flanigan 2023 - Task Contamination: Language Models May Not Be Few-Shot Anymore]]
   * LLMSanitize: [[https://arxiv.org/pdf/2404.00699.pdf|Ravaut et al 2024 - How Much are LLMs Contaminated? A Comprehensive Survey and the LLMSanitize Library]]   * LLMSanitize: [[https://arxiv.org/pdf/2404.00699.pdf|Ravaut et al 2024 - How Much are LLMs Contaminated? A Comprehensive Survey and the LLMSanitize Library]]
-  * [[https://arxiv.org/pdf/2405.00332|Zhang et al 2024 - A Careful Examination of Large Language Model Performance on Grade School Arithmetic]] 
   * [[https://arxiv.org/pdf/2404.18543|Drinkall et al 2024 - Time Machine GPT]]   * [[https://arxiv.org/pdf/2404.18543|Drinkall et al 2024 - Time Machine GPT]]
 +  * GSM1k: [[https://arxiv.org/pdf/2405.00332|Zhang et al 2024 - A Careful Examination of Large Language Model Performance on Grade School Arithmetic]] Re-evaluates GSM8K with a new dataset
  
 ==== Dependence on Number of Examples ==== ==== Dependence on Number of Examples ====
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   * [[https://arxiv.org/pdf/2211.15661.pdf|Akyürek et al 2022 - What learning algorithm is in-context learning? Investigations with linear models]]   * [[https://arxiv.org/pdf/2211.15661.pdf|Akyürek et al 2022 - What learning algorithm is in-context learning? Investigations with linear models]]
   * [[https://arxiv.org/pdf/2310.15916.pdf|Hendel et al 2023 - In-Context Learning Creates Task Vectors]]   * [[https://arxiv.org/pdf/2310.15916.pdf|Hendel et al 2023 - In-Context Learning Creates Task Vectors]]
 +  * [[https://arxiv.org/pdf/2505.05145|Hu et al 2025 - Understanding In-context Learning of Addition via Activation Subspaces]] Great paper. Fig 1 is awesome.
 +  * [[https://arxiv.org/pdf/2504.00132|Bakalova et al 2025 - Contextualize-then-Aggregate: Circuits for In-Context Learning in Gemma-2 2B]]
  
 ===== Datasets ===== ===== Datasets =====
nlp/prompting.1743814545.txt.gz · Last modified: 2025/04/05 00:55 by jmflanig
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