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πŸ“ TRAM: Bridging Trust Regions and Sharpness Aware Minimization πŸ§ πŸ“š"Proposes Trust Region Aware Minimization, which encourages...

https://creative.ai/@arxiv_cl/11...

πŸ“ TRAM: Bridging Trust Regions and Sharpness Aware Minimization πŸ§ πŸ“š

"Proposes Trust Region Aware Minimization, which encourages flat and smooth minima while maintaining pre-trained representations by using trust region bounds to inform SAM-style regularization on both of these optimization surfaces." [gal30b+] πŸ€–

βš™οΈ github.com/tomsherborne/tram_o
πŸ”— arxiv.org/abs/2310.03646v1

7.10.2023 14:08πŸ“ TRAM: Bridging Trust Regions and Sharpness Aware Minimization πŸ§ πŸ“š"Proposes Trust Region Aware Minimization, which encourages...
https://creative.ai/@arxiv_cl/11...

πŸ“ Neural Language Model Pruning for Automatic Speech Recognition πŸ§ πŸ“š"Proposes a variant of low-rank approximation suitable for...

https://creative.ai/@arxiv_cl/11...

πŸ“ Neural Language Model Pruning for Automatic Speech Recognition πŸ§ πŸ“š

"Proposes a variant of low-rank approximation suitable for incrementally compressing models and delivering multiple models with varied target sizes (e,g, 20Γ—, 50Γ— and 80Γ—)." [gal30b+] πŸ€–

πŸ”— arxiv.org/abs/2310.03424v1

7.10.2023 12:53πŸ“ Neural Language Model Pruning for Automatic Speech Recognition πŸ§ πŸ“š"Proposes a variant of low-rank approximation suitable for...
https://creative.ai/@arxiv_cl/11...

πŸ“ MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning πŸ“šπŸ‘ΎπŸ”­πŸ§ "Proposes a fine-tuning and...

https://creative.ai/@arxiv_cl/11...

πŸ“ MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning πŸ“šπŸ‘ΎπŸ”­πŸ§ 

"Proposes a fine-tuning and inference approach that enhances math reasoning in language models, enabling them to use code for modeling and deriving mathematical equations and, consequently, enhancing their mathematical ability." [gal30b+] πŸ€–

βš™οΈ github.com/mathllm/MathCoder
πŸ”— arxiv.org/abs/2310.03731v1

7.10.2023 11:53πŸ“ MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning πŸ“šπŸ‘ΎπŸ”­πŸ§ "Proposes a fine-tuning and...
https://creative.ai/@arxiv_cl/11...

πŸ“ Modular Speech-to-Text Translation for Zero-Shot Cross-Modal Transfer πŸ“š"The speech encoder is based on the wav2vec2 speech...

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πŸ“ Modular Speech-to-Text Translation for Zero-Shot Cross-Modal Transfer πŸ“š

"The speech encoder is based on the wav2vec2 speech representation and is trained with self-supervision to reconstruct masked portions of speech audio, while the text decoder is a causal Transformer network and is trained to autoregressively reconstruct target text." [gal30b+] πŸ€–

πŸ”— arxiv.org/abs/2310.03724v1

7.10.2023 10:38πŸ“ Modular Speech-to-Text Translation for Zero-Shot Cross-Modal Transfer πŸ“š"The speech encoder is based on the wav2vec2 speech...
https://creative.ai/@arxiv_cl/11...

πŸ“ A Long Way to Go: Investigating Length Correlations in RLHF πŸ“šπŸ§ "RLHF learns a reward model from human preference feedback on...

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πŸ“ A Long Way to Go: Investigating Length Correlations in RLHF πŸ“šπŸ§ 

"RLHF learns a reward model from human preference feedback on the outputs of a base model (e-commerce search, chat, question answering, summarization)." [gal30b+] πŸ€–

βš™οΈ github.com/PrasannS/rlhf-lengt
πŸ”— arxiv.org/abs/2310.03716v1

7.10.2023 07:53πŸ“ A Long Way to Go: Investigating Length Correlations in RLHF πŸ“šπŸ§ "RLHF learns a reward model from human preference feedback on...
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πŸ“ Agent Instructs Large Language Models to Be General Zero-Shot Reasoners πŸ“šπŸ‘ΎπŸ§ "Builds an autonomous agent to instruct the...

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πŸ“ Agent Instructs Large Language Models to Be General Zero-Shot Reasoners πŸ“šπŸ‘ΎπŸ§ 

"Builds an autonomous agent to instruct the reasoning process of large language models to further unleash their zero-shot reasoning abilities on a wide set of datasets spanning generation, classification, and reasoning." [gal30b+] πŸ€–

βš™οΈ github.com/wang-research-lab/a
πŸ”— arxiv.org/abs/2310.03710v1

7.10.2023 07:08πŸ“ Agent Instructs Large Language Models to Be General Zero-Shot Reasoners πŸ“šπŸ‘ΎπŸ§ "Builds an autonomous agent to instruct the...
https://creative.ai/@arxiv_cl/11...

πŸ“ DecoderLens: Layerwise Interpretation of Encoder-Decoder Transformers πŸ“š"DecoderLens allows the decoder cross-attention to...

https://creative.ai/@arxiv_cl/11...

πŸ“ DecoderLens: Layerwise Interpretation of Encoder-Decoder Transformers πŸ“š

"DecoderLens allows the decoder cross-attention to access all encoder outputs instead of only using the final encoder output, as is normally done in encoder-decoder models." [gal30b+] πŸ€–

πŸ”— arxiv.org/abs/2310.03686v1

7.10.2023 06:08πŸ“ DecoderLens: Layerwise Interpretation of Encoder-Decoder Transformers πŸ“š"DecoderLens allows the decoder cross-attention to...
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πŸ“ GoLLIE: Annotation Guidelines Improve Zero-Shot Information-Extraction πŸ“š"GoLLIE is fine-tuned from a large language model, to...

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πŸ“ GoLLIE: Annotation Guidelines Improve Zero-Shot Information-Extraction πŸ“š

"GoLLIE is fine-tuned from a large language model, to follow a given annotation guideline for a specific task, and it uses the resulting model to extract facts." [gal30b+] πŸ€–

βš™οΈ github.com/microsoft/DeepSpeed
πŸ”— arxiv.org/abs/2310.03668v1

7.10.2023 04:53πŸ“ GoLLIE: Annotation Guidelines Improve Zero-Shot Information-Extraction πŸ“š"GoLLIE is fine-tuned from a large language model, to...
https://creative.ai/@arxiv_cl/11...

πŸ“ Towards Robust and Generalizable Training: An Empirical Study of Noisy Slot Filling for Input Perturbations πŸ“šπŸ‘Ύ"Introduces a...

https://creative.ai/@arxiv_cl/11...

πŸ“ Towards Robust and Generalizable Training: An Empirical Study of Noisy Slot Filling for Input Perturbations πŸ“šπŸ‘Ύ

"Introduces a noise robustness evaluation dataset Noise-SF for slot filling task, which can help to evaluate the noise robustness of slot filling task, and provide training and evaluation data for robust models." [gal30b+] πŸ€–

βš™οΈ github.com/dongguanting/Noise-
πŸ”— arxiv.org/abs/2310.03518v1

7.10.2023 04:08πŸ“ Towards Robust and Generalizable Training: An Empirical Study of Noisy Slot Filling for Input Perturbations πŸ“šπŸ‘Ύ"Introduces a...
https://creative.ai/@arxiv_cl/11...

πŸ“ Tik-to-Tok: Translating Language Models One Token at a Time: An Embedding Initialization Strategy for Efficient Language Adaptation...

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πŸ“ Tik-to-Tok: Translating Language Models One Token at a Time: An Embedding Initialization Strategy for Efficient Language Adaptation πŸ“šπŸ‘Ύ

"We map tokens from the target tokenizer to semantically similar tokens from the source language tokenizer by using a word translation dictionary encompassing both the source and target languages, which is created automatically." [gal30b+] πŸ€–

πŸ”— arxiv.org/abs/2310.03477v1

7.10.2023 02:38πŸ“ Tik-to-Tok: Translating Language Models One Token at a Time: An Embedding Initialization Strategy for Efficient Language Adaptation...
https://creative.ai/@arxiv_cl/11...

πŸ“ Controllable Multi-Document Summarization: Coverage & Coherence Intuitive Policy with Large Language Model Based Rewards...

https://creative.ai/@arxiv_cl/11...

πŸ“ Controllable Multi-Document Summarization: Coverage & Coherence Intuitive Policy with Large Language Model Based Rewards πŸ“š

"A controllable content extraction scheme is trained with a novel coverage and coherence intuitive policy that is duly rewarded by an actively trained LLM, and then used for multi-document summarization." [gal30b+] πŸ€–

πŸ”— arxiv.org/abs/2310.03473v1

7.10.2023 00:53πŸ“ Controllable Multi-Document Summarization: Coverage & Coherence Intuitive Policy with Large Language Model Based Rewards...
https://creative.ai/@arxiv_cl/11...

πŸ“ LLM Based Multi-Document Summarization Exploiting Main-Event Biased Monotone Submodular Content Extraction πŸ“š"The main-event...

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πŸ“ LLM Based Multi-Document Summarization Exploiting Main-Event Biased Monotone Submodular Content Extraction πŸ“š

"The main-event biased monotone submodular function for content selection enables us to extract the most crucial information related to the main event from the document cluster, which is then rewritten to a coherent text by leveraging a large pre-trained language model." [gal30b+] πŸ€–

πŸ”— arxiv.org/abs/2310.03414v1

7.10.2023 00:08πŸ“ LLM Based Multi-Document Summarization Exploiting Main-Event Biased Monotone Submodular Content Extraction πŸ“š"The main-event...
https://creative.ai/@arxiv_cl/11...

πŸ“ Procedural Text Mining with Large Language Models πŸ“šπŸ‘Ύ"Works by leveraging the GPT-4 (Generative Pre-trained Transformer 4)...

https://creative.ai/@arxiv_cl/11...

πŸ“ Procedural Text Mining with Large Language Models πŸ“šπŸ‘Ύ

"Works by leveraging the GPT-4 (Generative Pre-trained Transformer 4) model to extract procedures from unstructured PDF text in an incremental question-answering fashion." [gal30b+] πŸ€–

βš™οΈ github.com/jd-coderepos/proc-t
πŸ”— arxiv.org/abs/2310.03376v1

6.10.2023 23:08πŸ“ Procedural Text Mining with Large Language Models πŸ“šπŸ‘Ύ"Works by leveraging the GPT-4 (Generative Pre-trained Transformer 4)...
https://creative.ai/@arxiv_cl/11...

πŸ“ Evaluating Hallucinations in Chinese Large Language Models πŸ“š"Establishes a benchmark named HalluQA for measuring the...

https://creative.ai/@arxiv_cl/11...

πŸ“ Evaluating Hallucinations in Chinese Large Language Models πŸ“š

"Establishes a benchmark named HalluQA for measuring the hallucination phenomenon in Chinese large language models and design a novel automated evaluation method using GPT-4 to judge whether a model output is hallucinated." [gal30b+] πŸ€–

βš™οΈ github.com/xiami2019/HalluQA
πŸ”— arxiv.org/abs/2310.03368v1

6.10.2023 21:23πŸ“ Evaluating Hallucinations in Chinese Large Language Models πŸ“š"Establishes a benchmark named HalluQA for measuring the...
https://creative.ai/@arxiv_cl/11...

πŸ“ Reformulating Domain Adaptation of Large Language Models as Adapt-Retrieve-Revise πŸ“š"Given a target domain like Chinese law, it...

https://creative.ai/@arxiv_cl/11...

πŸ“ Reformulating Domain Adaptation of Large Language Models as Adapt-Retrieve-Revise πŸ“š

"Given a target domain like Chinese law, it first continues learning on in-domain data to \textbf{adapt} an affordable 7B LLM to the target domain." [gal30b+] πŸ€–

πŸ”— arxiv.org/abs/2310.03328v1

6.10.2023 19:38πŸ“ Reformulating Domain Adaptation of Large Language Models as Adapt-Retrieve-Revise πŸ“š"Given a target domain like Chinese law, it...
https://creative.ai/@arxiv_cl/11...

πŸ“ Concise and Organized Perception Facilitates Large Language Models for Deductive Reasoning πŸ“šπŸ‘Ύ"Carefully analyzes the given...

https://creative.ai/@arxiv_cl/11...

πŸ“ Concise and Organized Perception Facilitates Large Language Models for Deductive Reasoning πŸ“šπŸ‘Ύ

"Carefully analyzes the given statements to efficiently identify the most pertinent information while eliminating redundancy, and then prompts the LLMs in a more organized form that adapts to the model's inference process." [gal30b+] πŸ€–

βš™οΈ github.com/asaparov/prontoqa
πŸ”— arxiv.org/abs/2310.03309v1

6.10.2023 18:23πŸ“ Concise and Organized Perception Facilitates Large Language Models for Deductive Reasoning πŸ“šπŸ‘Ύ"Carefully analyzes the given...
https://creative.ai/@arxiv_cl/11...

πŸ“ A New Dialogue Response Generation Agent for Large Language Models by Asking Questions to Detect User's Intentions πŸ“š"The...

https://creative.ai/@arxiv_cl/11...

πŸ“ A New Dialogue Response Generation Agent for Large Language Models by Asking Questions to Detect User's Intentions πŸ“š

"The open-domain dialogue system EDIT consists of a Question Generation (QG) module, an LLM-based QA module and a Knowledge-Enhanced Response Generation module (KG-RG)." [gal30b+] πŸ€–

πŸ”— arxiv.org/abs/2310.03293v1

6.10.2023 17:38πŸ“ A New Dialogue Response Generation Agent for Large Language Models by Asking Questions to Detect User's Intentions πŸ“š"The...
https://creative.ai/@arxiv_cl/11...

πŸ“ A Formalism and Approach for Improving Robustness of Large Language Models Using Risk-Adjusted Confidence Scores πŸ“š"A novel...

https://creative.ai/@arxiv_cl/11...

πŸ“ A Formalism and Approach for Improving Robustness of Large Language Models Using Risk-Adjusted Confidence Scores πŸ“š

"A novel method for reducing risk by adjusting LLM confidence scores using a novel calibration method called DwD and a novel evaluation method for assessing both low and high risk tasks." [gal30b+] πŸ€–

πŸ”— arxiv.org/abs/2310.03283v1

6.10.2023 16:38πŸ“ A Formalism and Approach for Improving Robustness of Large Language Models Using Risk-Adjusted Confidence Scores πŸ“š"A novel...
https://creative.ai/@arxiv_cl/11...

πŸ“ Unlock Predictable Scaling From Emergent Abilities πŸ“š"Discovers that small models, although they exhibit minor performance,...

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πŸ“ Unlock Predictable Scaling From Emergent Abilities πŸ“š

"Discovers that small models, although they exhibit minor performance, demonstrate critical and consistent task performance improvements that are not captured by conventional evaluation strategies due to insufficient measurement resolution." [gal30b+] πŸ€–

βš™οΈ github.com/openai/human-eval
πŸ”— arxiv.org/abs/2310.03262v1

6.10.2023 15:38πŸ“ Unlock Predictable Scaling From Emergent Abilities πŸ“š"Discovers that small models, although they exhibit minor performance,...
https://creative.ai/@arxiv_cl/11...

πŸ“ Can Large Language Models Be Good Path Planners? A Benchmark and Investigation on Spatial-Temporal Reasoning...

https://creative.ai/@arxiv_cl/11...

πŸ“ Can Large Language Models Be Good Path Planners? A Benchmark and Investigation on Spatial-Temporal Reasoning πŸ“š

"\textcolor{black}{Proposes a new benchmark, termed PPNL, to evaluate LLMs' spatial-temporal reasoning by formulating ``path planning'' tasks that require an LLM to navigate to target locations while avoiding obstacles and adhering to constraints." [gal30b+] πŸ€–

πŸ”— arxiv.org/abs/2310.03249v1

6.10.2023 14:38πŸ“ Can Large Language Models Be Good Path Planners? A Benchmark and Investigation on Spatial-Temporal Reasoning...
https://creative.ai/@arxiv_cl/11...
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