Every year, ACL (the Association for Computational Linguistics) offers a preview of where natural language processing (NLP) and large language models (LLMs) are headed in 2026. We observed several key themes that will directly influence how companies build, deploy, and evaluate AI systems.
Here are the five trends we see coming out of this year’s conference and key papers to watch.
1. Fairness and Bias Remain a Top Priority
Bias and alignment challenges are still front and centre, particularly when moving beyond the English language. Researchers are building new benchmarks to uncover gaps in multilingual alignment and confidence estimation. At Appen, we’re examining cultural nuance in our multilingual LLM translation research.
Key Takeaways:
- Explicit vs. implicit bias differ: LLMs may appear unbiased in self-reports but show stereotypes in behaviour.
- Gender-neutral translation remains difficult; models default to masculine pronouns in ambiguous cases.
- Reward models perform well in English but misalign with human preferences in other languages.
- Confidence estimation is weaker outside English, though native-language prompts help.
- Translation quality and language resource availability are critical for alignment.
Papers to explore:
- Explicit vs. Implicit: Investigating Social Bias in LLMs through Self-Reflection
- Translate With Care: Addressing Gender Bias, Neutrality, and Reasoning in Large Language Model Translations
- MLINGCONF: A Comprehensive Study of Multilingual Confidence Estimation on Large Language Models
- M-REWARDBENCH: Evaluating Reward Models in Multilingual Settings
2. Growing Multimodal Capabilities
Vision–language models aren’t just about describing images anymore. Researchers are probing abstract reasoning (e.g., multi-step visual puzzles) and building practical systems for real-world multimodal tasks like translating text embedded in images.
Key Takeaways:
- Benchmarks like MultiStAR introduce new ways to evaluate multimodal AI.
- Step-by-step evaluation metrics make it clearer where models break down.
- Real-world use cases (subtitles over complex backgrounds) require smarter pipelines that separate, translate, and reintegrate text.
Papers to explore:
- Beyond Perception: Evaluating Abstract Visual Reasoning through Multi-Stage Task
- Exploring In-Image Machine Translation with Real-World Background
3. LLM Reasoning Needs Verification
Chain-of-thought prompting has improved reasoning, but reliability is still a bottleneck. New approaches combine lightweight checks with heavier verification only when necessary, boosting both accuracy and efficiency.
Key Takeaways:
- Arithmetic ability in LLMs depends heavily on numerical precision – quantization may hurt performance more than scaling helps.
- Adaptive verification (cheap checks + selective deep verification) balances performance and cost.
- Benchmarks show 8–11% accuracy gains with 2–3× efficiency improvements.
Papers to explore:
- How Numerical Precision Affects Arithmetical Reasoning Capabilities of LLMs
- Derailer-Rerailer: Adaptive Verification for Efficient and Reliable Language Model Reasoning
4. Prioritising Efficiency Over Scale
Since the release of DeepSeek in early 2025, the trend towards leaner models has continued to inspire innovation. Researchers are looking for ways to compress, prune, and distill LLMs without losing accuracy. This makes large-scale AI more deployable in enterprise settings.
Key Takeaways:
- MoE (Mixture of Experts) pruning can reduce redundancy by grouping and removing overlapping experts.
- Bayesian distillation improves small LLMs’ performance by aligning them more closely with teacher models.
- Gains of 3–4% accuracy on small models make them far more competitive.
Papers to explore:
- Diversifying the Expert Knowledge for Task-Agnostic Pruning in Sparse Mixture-of-Experts
- BayesKD: Bayesian Knowledge Distillation for Compact LLMs in Constrained Fine-tuning Scenarios
5. Retrieval and Personalisation Are Getting Smarter
LLMs are increasingly used to improve information retrieval and dialogue systems. We see two emerging directions to watch: filtering hallucinations in query expansion and building persona-aware memory for more natural multi-session chat.
Key Takeaways:
- Filtering out hallucinations in small LM–generated documents boosts retrieval quality, rivaling much larger systems.
- Combining retrieval results from raw vs. LLM-augmented queries yields state-of-the-art sparse retrieval performance.
- Persona-aware dialogue frameworks improve consistency and engagement across sessions by combining knowledge graphs, memory banks, and hybrid architectures.
Papers to explore:
- GOLFer: Smaller LM-Generated Documents Hallucination Filter & Combiner for Query Expansion in Information Retrieval
- Exp4Fuse: A Rank Fusion Framework for Enhanced Sparse Retrieval using Large Language Model-based Query Expansion
- A Persona-Aware LLM-Enhanced Framework for Multi-Session Personalized Dialogue Generation
What This Means for Industry
ACL 2025 shows where the field is headed:
- Bias evaluation is becoming more sophisticated, and mitigation will require targeted fine-tuning.
- Multimodality is maturing, but abstract reasoning and complex real-world use cases remain challenging.
- Verification techniques may become standard in enterprise AI to balance reliability and cost.
- Research increasingly focuses on making compact LLMs viable for production deployment.
- Smarter retrieval and personalization systems will unlock more natural human-AI interactions.
For our AI community, the takeaway is clear. We’ve set our sights on fair, efficient, and contextually aware systems.
With 25+ years of AI expertise, Appen is a trusted partner for model builders around the world. Speak with an expert to learn how we support the AI lifecycle from development to deployment and fine-tuning.