- See Also
-
Links
- “Characterizing Attribution and Fluency Tradeoffs for Retrieval-Augmented Large Language Models”, Et Al 2023
- “Interactive-Chain-Prompting (INTERCPT): Ambiguity Resolution for Crosslingual Conditional Generation With Interaction”, Et Al 2023
- “Med-PaLM: Large Language Models Encode Clinical Knowledge”, Et Al 2022
- “Efficiently Scaling Transformer Inference”, Et Al 2022
- “Large Language Models Can Self-Improve”, Et Al 2022
- “FLAN: Scaling Instruction-Finetuned Language Models”, Et Al 2022
- “U-PaLM: Transcending Scaling Laws With 0.1% Extra Compute”, Et Al 2022
- “Challenging BIG-Bench Tasks (BBH) and Whether Chain-of-Thought Can Solve Them”, Et Al 2022
- “RARR: Attributed Text Generation via Post-hoc Research and Revision”, Et Al 2022
- “ReAct: Synergizing Reasoning and Acting in Language Models”, Et Al 2022
- “Language Models Are Multilingual Chain-of-Thought Reasoners”, Et Al 2022
- “AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model”, Et Al 2022
- “Inner Monologue: Embodied Reasoning through Planning With Language Models”, Et Al 2022
- “Solving Quantitative Reasoning Problems With Language Models”, Et Al 2022
- “Least-to-Most Prompting Enables Complex Reasoning in Large Language Models”, Et Al 2022
- “Unifying Language Learning Paradigms”, Et Al 2022
- “PaLM: Scaling Language Modeling With Pathways”, Et Al 2022
- “Do As I Can, Not As I Say (SayCan): Grounding Language in Robotic Affordances”, Et Al 2022
- “Pathways Language Model (PaLM): Scaling to 540 Billion Parameters for Breakthrough Performance”, 2022
- “PaLM § Figure 19: [Explaining a Joke / Inference Chaining] Each”Input” Was Independently Prepended With the Same 2-shot Exemplar Shown at the Top, And “Model Output” Shows the Greedy Decoding Output of PaLM 540B. The Two Exemplar Jokes Are Known Jokes (explanations Written by Authors), While All Evaluated Jokes Were Written by the Authors. Of Course, These Jokes Do Share Abstract Premises With Existing Jokes (wordplay, Reliability, Humorous Analogies, Reversal-of-expectations). The Inference Chaining Examples Were Also Written by the Authors.”
- Miscellaneous
- Link Bibliography
See Also
Links
“Characterizing Attribution and Fluency Tradeoffs for Retrieval-Augmented Large Language Models”, Et Al 2023
“Characterizing Attribution and Fluency Tradeoffs for Retrieval-Augmented Large Language Models”, 2023-02-11 (; similar)
“Interactive-Chain-Prompting (INTERCPT): Ambiguity Resolution for Crosslingual Conditional Generation With Interaction”, Et Al 2023
“Interactive-Chain-Prompting (INTERCPT): Ambiguity Resolution for Crosslingual Conditional Generation with Interaction”, 2023-01-24 (; similar)
“Med-PaLM: Large Language Models Encode Clinical Knowledge”, Et Al 2022
“Med-PaLM: Large Language Models Encode Clinical Knowledge”, 2022-12-26 (; similar; bibliography)
“Efficiently Scaling Transformer Inference”, Et Al 2022
“Efficiently Scaling Transformer Inference”, 2022-11-09 (; similar; bibliography)
“Large Language Models Can Self-Improve”, Et Al 2022
“Large Language Models Can Self-Improve”, 2022-10-20 (; similar; bibliography)
“FLAN: Scaling Instruction-Finetuned Language Models”, Et Al 2022
“FLAN: Scaling Instruction-Finetuned Language Models”, 2022-10-20 (; similar; bibliography)
“U-PaLM: Transcending Scaling Laws With 0.1% Extra Compute”, Et Al 2022
“U-PaLM: Transcending Scaling Laws with 0.1% Extra Compute”, 2022-10-20 (; similar; bibliography)
“Challenging BIG-Bench Tasks (BBH) and Whether Chain-of-Thought Can Solve Them”, Et Al 2022
“Challenging BIG-Bench Tasks (BBH) and Whether Chain-of-Thought Can Solve Them”, 2022-10-17 (; similar; bibliography)
“RARR: Attributed Text Generation via Post-hoc Research and Revision”, Et Al 2022
“RARR: Attributed Text Generation via Post-hoc Research and Revision”, 2022-10-17 (; similar; bibliography)
“ReAct: Synergizing Reasoning and Acting in Language Models”, Et Al 2022
“ReAct: Synergizing Reasoning and Acting in Language Models”, 2022-10-06 (; similar)
“Language Models Are Multilingual Chain-of-Thought Reasoners”, Et Al 2022
“Language Models are Multilingual Chain-of-Thought Reasoners”, 2022-10-06 (; similar; bibliography)
“AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model”, Et Al 2022
“AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model”, 2022-08-02 (; similar; bibliography)
“Inner Monologue: Embodied Reasoning through Planning With Language Models”, Et Al 2022
“Inner Monologue: Embodied Reasoning through Planning with Language Models”, 2022-07-12 (; similar; bibliography)
“Solving Quantitative Reasoning Problems With Language Models”, Et Al 2022
“Solving Quantitative Reasoning Problems with Language Models”, 2022-06-29 (; similar)
“Least-to-Most Prompting Enables Complex Reasoning in Large Language Models”, Et Al 2022
“Least-to-Most Prompting Enables Complex Reasoning in Large Language Models”, 2022-05-21 (; similar; bibliography)
“Unifying Language Learning Paradigms”, Et Al 2022
“Unifying Language Learning Paradigms”, 2022-05-10 (; similar; bibliography)
“PaLM: Scaling Language Modeling With Pathways”, Et Al 2022
“PaLM: Scaling Language Modeling with Pathways”, 2022-04-05 (; similar; bibliography)
“Do As I Can, Not As I Say (SayCan): Grounding Language in Robotic Affordances”, Et Al 2022
“Do As I Can, Not As I Say (SayCan): Grounding Language in Robotic Affordances”, 2022-04-04 (; similar; bibliography)
“Pathways Language Model (PaLM): Scaling to 540 Billion Parameters for Breakthrough Performance”, 2022
Miscellaneous
Link Bibliography
-
https://arxiv.org/abs/2212.13138#google: “Med-PaLM: Large Language Models Encode Clinical Knowledge”, : -
https://arxiv.org/abs/2211.05102#google: “Efficiently Scaling Transformer Inference”, : -
https://arxiv.org/abs/2210.11610#google: “Large Language Models Can Self-Improve”, Jiaxin Huang, Shixiang Shane Gu, Le Hou, Yuexin Wu, Xuezhi Wang, Hongkun Yu, Jiawei Han: -
https://arxiv.org/abs/2210.11416#google: “FLAN: Scaling Instruction-Finetuned Language Models”, : -
https://arxiv.org/abs/2210.11399#google: “U-PaLM: Transcending Scaling Laws With 0.1% Extra Compute”, : -
https://arxiv.org/abs/2210.09261#google: “Challenging BIG-Bench Tasks (BBH) and Whether Chain-of-Thought Can Solve Them”, : -
https://arxiv.org/abs/2210.08726#google: “RARR: Attributed Text Generation via Post-hoc Research and Revision”, : -
https://arxiv.org/abs/2210.03057#google: “Language Models Are Multilingual Chain-of-Thought Reasoners”, : -
https://arxiv.org/abs/2208.01448#amazon: “AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model”, : -
https://arxiv.org/abs/2207.05608#google: “Inner Monologue: Embodied Reasoning through Planning With Language Models”, : -
https://arxiv.org/abs/2205.10625#google: “Least-to-Most Prompting Enables Complex Reasoning in Large Language Models”, : -
https://arxiv.org/abs/2205.05131#google: “Unifying Language Learning Paradigms”, : -
https://arxiv.org/abs/2204.02311#google: “PaLM: Scaling Language Modeling With Pathways”, : -
https://arxiv.org/abs/2204.01691#google: “Do As I Can, Not As I Say (SayCan): Grounding Language in Robotic Affordances”, :