Search: instruction-following - ai.jp.net

research #llm 📝 BlogAnalyzed: Jan 12, 2026 23:45

Reverse-Engineering Prompts: Insights into OpenAI Engineer Techniques

Published:Jan 12, 2026 23:44

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1 min read

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Qiita AI

Analysis

The article hints at a sophisticated prompting methodology used by OpenAI engineers, focusing on backward design. This reverse-engineering approach could signify a deeper understanding of LLM capabilities and a move beyond basic instruction-following, potentially unlocking more complex applications.

Key Takeaways

•The article discusses prompt engineering techniques used by OpenAI engineers.
•It highlights a reverse-engineering approach to prompt design.
•The source is a discussion on a Reddit PromptEngineering community.

Reference

“The post discusses a prompt design approach that works backward from the finished product.”

Permalink Qiita AI

Research Paper #Large Vision-Language Models (LVLMs), Instruction Following, Fine-tuning 🔬 ResearchAnalyzed: Jan 3, 2026 18:39

LVLMs Struggle with Instruction Following After Fine-tuning

Published:Dec 29, 2025 16:12

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1 min read

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ArXiv

Analysis

This paper addresses a critical issue in the development of Large Vision-Language Models (LVLMs): the degradation of instruction-following capabilities after fine-tuning. It highlights a significant problem where models lose their ability to adhere to instructions, a core functionality of the underlying Large Language Model (LLM). The study's importance lies in its quantitative demonstration of this decline and its investigation into the causes, specifically the impact of output format specification during fine-tuning. This research provides valuable insights for improving LVLM training methodologies.

Key Takeaways

•LVLMs often lose instruction-following ability after fine-tuning with common datasets.
•Specifying output format during fine-tuning improves instruction following.
•Including output format instructions in training data can mitigate the decline in instruction-following abilities.

Reference

“LVLMs trained with datasets, including instructions on output format, tend to follow instructions more accurately than models that do not.”

Permalink ArXiv

Paper #LLM 🔬 ResearchAnalyzed: Jan 3, 2026 16:22

Width Pruning in Llama-3: Enhancing Instruction Following by Reducing Factual Knowledge

Published:Dec 27, 2025 18:09

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1 min read

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ArXiv

Analysis

This paper challenges the common understanding of model pruning by demonstrating that width pruning, guided by the Maximum Absolute Weight (MAW) criterion, can selectively improve instruction-following capabilities while degrading performance on tasks requiring factual knowledge. This suggests that pruning can be used to trade off knowledge for improved alignment and truthfulness, offering a novel perspective on model optimization and alignment.

Key Takeaways

•Width pruning, guided by MAW, reveals a dichotomy: knowledge degrades while instruction-following improves.
•Expansion ratio is a critical architectural parameter that modulates cognitive capabilities.
•Inverse correlation between factual knowledge and truthfulness is observed.
•Pruned configurations offer energy efficiency gains but may impact latency in single-request scenarios.

Reference

“Instruction-following capabilities improve substantially (+46% to +75% in IFEval for Llama-3.2-1B and 3B models).”

Permalink ArXiv

Paper #recommendation systems, LLM, e-commerce 🔬 ResearchAnalyzed: Jan 3, 2026 16:30

OxygenREC: Instruction-Following Generative Framework for E-commerce Recommendation

Published:Dec 26, 2025 21:13

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1 min read

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ArXiv

Analysis

This paper introduces OxygenREC, an industrial recommendation system designed to address limitations in existing Generative Recommendation (GR) systems. It leverages a Fast-Slow Thinking architecture to balance deep reasoning capabilities with real-time performance requirements. The key contributions are a semantic alignment mechanism for instruction-enhanced generation and a multi-scenario scalability solution using controllable instructions and policy optimization. The paper aims to improve recommendation accuracy and efficiency in real-world e-commerce environments.

Key Takeaways

•Addresses limitations of traditional and generative recommendation systems.
•Employs a Fast-Slow Thinking architecture for efficient deep reasoning.
•Introduces a semantic alignment mechanism for instruction-guided generation.
•Offers a solution for multi-scenario scalability using controllable instructions and policy optimization.
•Aims to improve recommendation accuracy, efficiency, and resource utilization in e-commerce.

Reference

“OxygenREC leverages Fast-Slow Thinking to deliver deep reasoning with strict latency and multi-scenario requirements of real-world environments.”

Permalink ArXiv

Research #LLM 🔬 ResearchAnalyzed: Jan 10, 2026 09:40

CIFE: A New Benchmark for Code Instruction-Following Evaluation

Published:Dec 19, 2025 09:43

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1 min read

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ArXiv

Analysis

This article introduces CIFE, a new benchmark designed to evaluate how well language models follow code instructions. The work addresses a crucial need for more robust evaluation of LLMs in code-related tasks.

Key Takeaways

•CIFE provides a standardized method for assessing LLM performance in code-related tasks.
•The benchmark can help identify strengths and weaknesses of different language models.
•This research contributes to the development of more reliable and efficient AI systems for code generation and understanding.

Reference

“CIFE is a benchmark for evaluating code instruction-following.”

Permalink ArXiv

Research #LLM 🔬 ResearchAnalyzed: Jan 10, 2026 11:18

Reassessing Language Model Reliability in Instruction Following

Published:Dec 15, 2025 02:57

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1 min read

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ArXiv

Analysis

This ArXiv article likely investigates the consistency and accuracy of language models when tasked with following instructions. Analyzing this aspect is crucial for the safe and effective deployment of AI, particularly in applications requiring precise command execution.

Key Takeaways

•The research likely identifies potential failure points in instruction-following.
•The study probably evaluates different model architectures and training strategies.
•Findings could inform best practices for prompting and model deployment.

Reference

“The article's focus is on the reliability of language models when used for instruction following.”

Permalink ArXiv

Research #LLM 🔬 ResearchAnalyzed: Jan 10, 2026 13:19

DoLA Adaptations Boost Instruction-Following in Seq2Seq Models

Published:Dec 3, 2025 13:54

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1 min read

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ArXiv

Analysis

This ArXiv paper explores the use of DoLA adaptations to enhance instruction-following capabilities in Seq2Seq models, specifically targeting T5. The research offers insights into potential improvements in model performance and addresses a key challenge in NLP.

Key Takeaways

•DoLA adaptations are investigated for improving instruction-following.
•The study specifically focuses on applying DoLA to the T5 model.
•The work potentially contributes to improved performance in NLP tasks.

Reference

“The research focuses on DoLA adaptations for the T5 Seq2Seq model.”

Permalink ArXiv

Research #LLM 🔬 ResearchAnalyzed: Jan 10, 2026 13:28

New Benchmark Measures LLM Instruction Following Under Data Compression

Published:Dec 2, 2025 13:25

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1 min read

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ArXiv

Analysis

This ArXiv paper introduces a novel benchmark that differentiates between compliance with constraints and semantic accuracy in instruction following for Large Language Models (LLMs). This is a crucial step towards understanding how LLMs perform when data is compressed, mirroring real-world scenarios where bandwidth is limited.

Key Takeaways

•The research provides a new benchmark for evaluating LLMs.
•The benchmark focuses on scenarios involving data compression.
•It aims to separate constraint compliance from semantic accuracy.

Reference

“The paper focuses on evaluating instruction-following under data compression.”

Permalink ArXiv

Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 09:10

LLM CHESS: Benchmarking Reasoning and Instruction-Following in LLMs through Chess

Published:Dec 1, 2025 18:51

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1 min read

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ArXiv

Analysis

This article likely presents a research paper that uses chess as a benchmark to evaluate the reasoning and instruction-following capabilities of Large Language Models (LLMs). Chess provides a complex, rule-based environment suitable for assessing these abilities. The use of ArXiv suggests this is a pre-print or published research.

Key Takeaways

•The research focuses on evaluating LLMs.
•Chess is used as a testing ground.
•The study assesses reasoning and instruction-following.

Reference

“”

Permalink ArXiv

Research #LLM 🔬 ResearchAnalyzed: Jan 10, 2026 13:47

Novel Approach to Curbing Indirect Prompt Injection in LLMs

Published:Nov 30, 2025 16:29

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1 min read

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ArXiv

Analysis

The research, available on ArXiv, proposes a method for mitigating indirect prompt injection, a significant security concern in large language models. The analysis of instruction-following intent represents a promising step towards enhancing LLM safety.

Key Takeaways

•Addresses the problem of indirect prompt injection.
•Utilizes instruction-following intent analysis.
•Published on ArXiv, indicating early stage research.

Reference

“The research focuses on mitigating indirect prompt injection, a significant vulnerability.”

Permalink ArXiv

Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 07:47

Minimal-Edit Instruction Tuning for Low-Resource Indic GEC

Published:Nov 28, 2025 21:38

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1 min read

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ArXiv

Analysis

This article likely presents a research paper on improving grammatical error correction (GEC) for Indic languages (Indian languages) using instruction tuning with minimal edits. The focus is on addressing the challenge of limited data resources for these languages. The research probably explores techniques to fine-tune language models effectively with minimal modifications to the training data or model architecture. The use of 'instruction tuning' suggests the researchers are leveraging the power of instruction-following capabilities of large language models (LLMs).

Key Takeaways

•Focus on grammatical error correction (GEC) for Indic languages.
•Addresses the challenge of low-resource data.
•Employs instruction tuning techniques.
•Emphasizes minimal edits to training data or model architecture.

Reference

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Permalink ArXiv

Ethics #LLM 🔬 ResearchAnalyzed: Jan 10, 2026 14:12

Expert LLMs: Instruction Following Undermines Transparency

Published:Nov 26, 2025 16:41

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1 min read

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ArXiv

Analysis

This research highlights a crucial flaw in expert-persona LLMs, demonstrating how adherence to instructions can override the disclosure of important information. This finding underscores the need for robust mechanisms to ensure transparency and prevent manipulation in AI systems.

Key Takeaways

•Expert-persona LLMs are vulnerable to manipulation due to instruction-following.
•Transparency mechanisms are crucial for mitigating risks.
•Further research is needed to improve disclosure in AI systems.

Reference

“Instruction-following can override disclosure.”

Permalink ArXiv

Research #Dialogue 🔬 ResearchAnalyzed: Jan 10, 2026 14:33

New Benchmark for Evaluating Complex Instruction-Following in Dialogues

Published:Nov 20, 2025 02:10

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1 min read

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ArXiv

Analysis

This research introduces a new benchmark, TOD-ProcBench, specifically designed to assess how well AI models handle intricate instructions in task-oriented dialogues. The focus on complex instructions distinguishes this benchmark and addresses a crucial area in AI development.

Key Takeaways

•TOD-ProcBench is a new benchmark for evaluating AI models.
•The benchmark focuses on complex instruction-following.
•The research contributes to improved AI performance in task-oriented dialogues.

Reference

“TOD-ProcBench benchmarks complex instruction-following in Task-Oriented Dialogues.”

Permalink ArXiv

Research #LLMs 🔬 ResearchAnalyzed: Jan 10, 2026 14:38

ConInstruct: Benchmarking LLMs on Conflict Detection and Resolution in Instructions

Published:Nov 18, 2025 10:49

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1 min read

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ArXiv

Analysis

The study's focus on instruction-following is critical for safety and usability of LLMs, and the methodology of evaluating conflict detection is well-defined. However, the article's lack of concrete results beyond the abstract prevents a deeper understanding of its implications.

Key Takeaways

•ConInstruct proposes a new benchmark for evaluating LLMs on instruction understanding.
•The research focuses on the critical task of conflict detection and resolution.
•The paper is likely relevant to efforts to improve the safety and reliability of LLMs.

Reference

“ConInstruct evaluates Large Language Models on their ability to detect and resolve conflicts within instructions.”

Permalink ArXiv

Research #llm 📝 BlogAnalyzed: Jan 3, 2026 06:39

Announcing Llama 3.3 70B, with enhanced reasoning, mathematics, and instruction-following on Together AI

Published:Dec 6, 2024 00:00

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1 min read

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Together AI

Analysis

The article announces the release of Llama 3.3 70B, highlighting improvements in reasoning, mathematics, and instruction-following capabilities. It is likely a press release or announcement from Together AI, the platform where the model is available. The focus is on the model's technical advancements.

Key Takeaways

•Llama 3.3 70B is a new large language model.
•It features improved reasoning, mathematics, and instruction-following.
•The model is available on Together AI.

Reference

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Permalink Together AI

Research #llm 🏛️ OfficialAnalyzed: Dec 24, 2025 12:01

Cappy: Small Scorer Boosts Large Multi-Task Language Models

Published:Mar 14, 2024 19:38

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1 min read

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Google Research

Analysis

This article from Google Research introduces Cappy, a small scorer designed to improve the performance of large multi-task language models (LLMs) like FLAN and OPT-IML. The article highlights the challenges associated with operating these massive models, including high computational costs and memory requirements. Cappy aims to address these challenges by providing a more efficient way to evaluate and refine the outputs of these LLMs. The focus on instruction-following and task-wise generalization is crucial for advancing NLP capabilities. Further details on Cappy's architecture and performance metrics would strengthen the article.

Key Takeaways

•Multi-task LLMs are trained on instruction-response pairs.
•These models exhibit task-wise generalization capabilities.
•Operating large LLMs is computationally expensive.

Reference

“Large language model (LLM) advancements have led to a new paradigm that unifies various natural language processing (NLP) tasks within an instruction-following framework.”

Permalink Google Research

Research #llm 👥 CommunityAnalyzed: Jan 4, 2026 07:28

Stanford Alpaca: An Instruction-following LLaMA model

Published:Mar 13, 2023 17:29

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1 min read

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Hacker News

Analysis

The article announces the development of Stanford Alpaca, an instruction-following model based on LLaMA. The source is Hacker News, suggesting a tech-focused audience. The focus is on the model's ability to follow instructions, implying advancements in natural language processing and potentially improved user interaction with AI.

Key Takeaways

•Stanford Alpaca is a new instruction-following model.
•It is based on the LLaMA model.
•The announcement is on Hacker News, indicating a tech-savvy audience.

Reference

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Permalink Hacker News

Reverse-Engineering Prompts: Insights into OpenAI Engineer Techniques

Analysis

Key Takeaways

LVLMs Struggle with Instruction Following After Fine-tuning

Analysis

Key Takeaways

Width Pruning in Llama-3: Enhancing Instruction Following by Reducing Factual Knowledge

Analysis

Key Takeaways

OxygenREC: Instruction-Following Generative Framework for E-commerce Recommendation

Analysis

Key Takeaways

CIFE: A New Benchmark for Code Instruction-Following Evaluation

Analysis

Key Takeaways

Reassessing Language Model Reliability in Instruction Following

Analysis

Key Takeaways

DoLA Adaptations Boost Instruction-Following in Seq2Seq Models

Analysis

Key Takeaways

New Benchmark Measures LLM Instruction Following Under Data Compression

Analysis

Key Takeaways

LLM CHESS: Benchmarking Reasoning and Instruction-Following in LLMs through Chess

Analysis

Key Takeaways

Novel Approach to Curbing Indirect Prompt Injection in LLMs

Analysis

Key Takeaways

Minimal-Edit Instruction Tuning for Low-Resource Indic GEC

Analysis

Key Takeaways

Expert LLMs: Instruction Following Undermines Transparency

Analysis

Key Takeaways

New Benchmark for Evaluating Complex Instruction-Following in Dialogues

Analysis

Key Takeaways

ConInstruct: Benchmarking LLMs on Conflict Detection and Resolution in Instructions

Analysis

Key Takeaways

Announcing Llama 3.3 70B, with enhanced reasoning, mathematics, and instruction-following on Together AI

Analysis

Key Takeaways

Cappy: Small Scorer Boosts Large Multi-Task Language Models

Analysis

Key Takeaways

Stanford Alpaca: An Instruction-following LLaMA model

Analysis

Key Takeaways

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