Research map / Large language models
Language, safety and interpretability: research map
2,928 accepted papers on Language, safety and interpretability in Large language models, from ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026), grouped into 6 clusters and 26 approaches. The busiest year so far is 2026.
Within Large language models, its share shrank from 35.2% in 2023–24 to 26.9% in 2025–26 (700 → 1,847 papers at ICML, NeurIPS, CVPR and AAAI, the venues with data for all four years).
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Approaches and key papers
prompt · uncertainty · shot · 1,303 papers
Approaches in this cluster:
- LLM unlearning and benchmarks (298 papers)
Evaluate LLMs on unlearning, fallacies, and procedure generalization, with adaptation to domains. - LLM pruning and scaling data (285 papers)
Prune LLMs after training and study data effects on scaling and collapse. - Controlled text generation (220 papers)
Steer generation with plug-in attribute models, instructions, and token-level input refinement. - Faithfulness of LLM explanations (169 papers)
Evaluate whether natural-language explanations are faithful using counterfactuals and simulatability. - Data and evaluation methodology (136 papers)
Assess datasets, evaluation failures, and summarization with description-length and data-curation tools. - In-context learning analysis (95 papers)
Explain and extend in-context learning via latent-variable views, many-shot, and kNN prompting. - Parallel and early-exit decoding (100 papers)
Speed up decoding through parallel, layer-adaptive, and diffusion-based generation.
Most cited and most cited since 2024:
- GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding (ICLR 2019 · 4,101 citations)
- XLNet: Generalized Autoregressive Pretraining for Language Understanding (NeurIPS 2019 · 1,824 citations)
- Enhancing Job Recommendation through LLM-Based Generative Adversarial Networks (AAAI 2024 · 82 citations)
- Bootstrapping Large Language Models for Radiology Report Generation (AAAI 2024 · 72 citations)
languages · pretraining · multilingual · 456 papers
Approaches in this cluster:
- Pruning and circuits in LLMs (135 papers)
Prune or extract task-specific circuits and structure from large language models. - Reasoning mechanisms and failure modes (114 papers)
Analyze symbolic mechanisms, attention glitches, and hallucination in LLM reasoning. - Linguistic encoding in LLM representations (123 papers)
Probe how LLM embeddings encode syntax, semantics, and brain-aligned information. - Task vectors and steering (84 papers)
Locate task representations in attention heads and steer models via representation interventions.
Most cited and most cited since 2024:
- Incorporating Context into Language Encoding Models for fMRI (NeurIPS 2018 · 163 citations)
- Measuring abstract reasoning in neural networks (ICML 2018 · 141 citations)
- LLMs are Good Sign Language Translators (CVPR 2024 · 70 citations)
- Fluctuation-Based Adaptive Structured Pruning for Large Language Models (AAAI 2024 · 43 citations)
natural language · speech · generation · 378 papers
Approaches in this cluster:
- Text generation and evaluation metrics (79 papers)
Study embeddings, metrics, and language-model-based evaluation for natural language generation. - Speech and audio language models (111 papers)
Combine large language models with speech and audio understanding and generation. - Multilingual LLM data and culture (69 papers)
Allocate multilingual data, select pretraining data, and adapt models to cultural differences. - Sign language datasets and translation (23 papers)
Collect sign language datasets and improve translation with multilingual and monolingual data. - Sign and cross-lingual translation (96 papers)
Translates sign and spoken languages using large language models and multilingual pretraining.
Most cited and most cited since 2024:
- Improving Sign Language Translation With Monolingual Data by Sign Back-Translation (CVPR 2021 · 277 citations)
- Data Noising as Smoothing in Neural Network Language Models (ICLR 2017 · 213 citations)
- AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Head (AAAI 2024 · 114 citations)
- CultureLLM: Incorporating Cultural Differences into Large Language Models (NeurIPS 2024 · 26 citations)
safety · jailbreak · harmful · 340 papers
Approaches in this cluster:
- Uncertainty quantification for LLMs (136 papers)
Quantify confidence and uncertainty to benchmark and evaluate language models. - Statistical frameworks for LLM evaluation (124 papers)
Use conformal prediction, ranking models, and noise analysis for valid LLM evaluation. - Bias measurement and mitigation (59 papers)
Measure and reduce gender, geographic, and implicit bias in LLMs. - Jailbreak elicitation and risk (21 papers)
Elicit harmful outputs through interaction, many-shot, and weak-to-strong jailbreaks.
Most cited and most cited since 2024:
- The Curious Case of Neural Text Degeneration (ICLR 2020 · 1,122 citations)
- Towards Understanding and Mitigating Social Biases in Language Models (ICML 2021 · 124 citations)
- Improving Factuality and Reasoning in Language Models through Multiagent Debate (ICML 2024 · 94 citations)
- Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration (NeurIPS 2024 · 28 citations)
watermarking · generated text · watermarks · 228 papers
Approaches in this cluster:
- Text watermarking (80 papers)
Embed detectable, robust watermarks into LLM output with provable guarantees. - Zero-shot machine-text detection (82 papers)
Detect LLM-generated text with perplexity-based and reward-based zero-shot scores. - Data and hallucination detection (66 papers)
Detect pretraining data, hallucinations, and unsafe training data using token-level and attribution signals.
Most cited and most cited since 2024:
- Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News Detection (AAAI 2024 · 221 citations)
- A Holistic Approach to Undesired Content Detection in the Real World (AAAI 2023 · 119 citations)
- Position: Will we run out of data? Limits of LLM scaling based on human-generated data (ICML 2024 · 83 citations)
- Position: On the Possibilities of AI-Generated Text Detection (ICML 2024 · 53 citations)
representations · interpretability · heads · 223 papers
Approaches in this cluster:
- Safety alignment of LLMs (123 papers)
Study how safety training, safeguards, and unsafe-data forgetting shape LLM behavior. - Mechanistic analysis of attention and representations (43 papers)
Trace attention heads and representation editing to explain in-context learning and model behavior. - Jailbreak attacks and defenses (57 papers)
Craft automated, multi-turn, and out-of-distribution jailbreaks and defend against them.
Most cited and most cited since 2024:
- Jailbroken: How Does LLM Safety Training Fail? (NeurIPS 2023 · 130 citations)
- Anti-efficient encoding in emergent communication (NeurIPS 2019 · 68 citations)
- Tree of Attacks: Jailbreaking Black-Box LLMs Automatically (NeurIPS 2024 · 55 citations)
- AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models (ICLR 2024 · 27 citations)
Related topics in Large language models
- Reasoning and chain of thought (1,557)
- Instruction and fine-tuning (871)
- Retrieval and knowledge (1,064)
- Agents, code and math (1,722)
- Alignment and preferences (665)
- AI and society (827)