United States

Accurate job grading and evaluation are essential for ensuring fair compensation in Human Resources (HR) planning. In this research, we propose to improve job evaluation by semi-automating a manual, time-consuming, and inconsistent process with text-based classification models. We address three prediction tasks: job title classification, grading, and compensation prediction. For job title classification, we fine-tune a RoBERTa model for classification and use Gemini to generate synthetic job descriptions for rare job titles. For grade and compensation prediction, we compare TF-IDF and transformer-based embeddings (DistilRoBERTa, MPNet, MiniLM) in combination with deep neural networks and tree-based models (Random Forest, XGBoost). We optimize all models using grid search with hyperparameter tuning and cross-validation. The results show that job title classification by RoBERTa with Gemini-generated descriptions works well with an accuracy of about 97%. In our regression experiments, our models get promising results: for grade prediction, a tuned TF-IDF + XGBoost model achieves a mean absolute error (MAE) of 0.185, and for annual salary prediction, MiniLM embeddings with XGBoost get an MAE of €1,587. These findings demonstrate that a semi-automated pipeline can enhance traditional manual processes by boosting consistency, speeding up HR workflows, and reducing biased assessments.

ACL 2026

Enhancing Job Evaluation with Data Augmentation and Text Classification

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The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

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The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

Optimizing large language models for industrial sales requires balancing long-term commercial objectives (e.g., conversion rate) with immediate linguistic constraints such as fluency and compliance. Conventional reinforcement learning often merges these heterogeneous goals into a single reward, causing high-magnitude session-level rewards to overwhelm subtler turn-level signals, which leads to unstable training or reward hacking.
To address this issue, we propose **Dual-Horizon Credit Assignment (DuCA)**, a framework that disentangles optimization across time scales. Its core, **Horizon-Independent Advantage Normalization (HIAN)**, separately normalizes advantages from turn-level and session-level rewards before fusion, ensuring balanced gradient contributions from both immediate and long-term objectives to the policy update.
Extensive experiments with a high-fidelity user simulator show DuCA outperforms the state-of-the-art GRPO baseline, achieving a 6.82% relative improvement in conversion rate, reducing inter-sentence repetition by 82.28%, and lowering identity detection rate by 27.35%, indicating a substantial improvement for an industrial sales scenario that effectively balances the dual demands of strategic performance and naturalistic language generation.

Harmonizing Dense and Sparse Signals in Multi-turn RL: Dual-Horizon Credit Assignment for Industrial Sales Agents

Matching person names across heterogeneous records is a core challenge in entity resolution, especially within linguistically and culturally complex environments. Variations in naming conventions, inconsistent transliteration across scripts, and frequent data entry errors make it difficult to unify user identities, an essential requirement for Know Your Customer (KYC) compliance. While Large Language Models have shown promise in understanding natural language, they often struggle with the structured ambiguity present in such domain-specific settings. This paper introduces Structure-Guided Entity Resolution (SGER), a novel framework that fine-tunes an LLM through a two-phase curriculum. The model is first trained to parse the grammatical and semantic structure of personal names, then optimized for the downstream task of binary entity matching. We evaluate SGER in the challenging context of Indian identity data, one of the most linguistically diverse and noisy environments globally. SGER achieves 99.02% accuracy and an F1 of 0.994 on a held-out set of 50,000 real-world pairs, outperforming GPT-4o few-shot prompting and single-stage fine-tuning baselines. The system is fully deployed in production at a major fantasy sports platform, serving 250M+ users. Our results demonstrate that curriculum-guided training enables robust, high-precision entity resolution in real-world multilingual systems at scale.

Structure-Guided Entity Resolution: Fine-Tuning LLMs for Robust Name Matching in Complex Linguistic Contexts

Conversational news recommendation requires grounding each suggestion in a rapidly evolving article corpus while addressing implicit user intents that lack explicit retrievable keywords. To characterize this scenario, we identify 6 intent types from production dialogues: five are implicit and pose fundamental challenges to standard RAG pipelines, forming a critical retrieve-first bottleneck. To address these issues, we introduce intent-driven Semantic ID (SID) generation under a Generate-then-Match paradigm. With two-stage training that consists of multi-task SID alignment and GPT-4 Chain-of-Thought distillation, an LLM maps diverse intents to hierarchical SID prefixes, which are then fuzzy-matched to the current news pool to guarantee fully grounded recommendations. Profile-Aware Dual-Signal Reasoning (PADR) further enables cold-start users to obtain valid recommendations using only profiles. On a mainstream Chinese news platform, our 7B model achieves 0% hallucination and 12.4% L1 match in the 152K open-generation SID space ($4 \times$ random baseline). It matches GPT-4+Hybrid RAG on L1 while surpassing it on finer-grained metrics (L2 $2 \times$, Category +1.2pp) at $\sim 100 \times$ lower cost. Cold-start users, where existing baselines score 0%, achieve 18.0% L1 ($6 \times$ random), the highest among all user groups.

Intent-Driven Semantic ID Generation for Grounded Conversational News Recommendation

Entity extraction from spoken customer–agent conversations is increasingly driving automation in contact centers. In these settings, extraction errors can trigger incorrect system actions, including database updates, verification failures, and unintended workflow execution. While prior work has examined the impact of transcription noise and cross-turn reasoning, it has not systematically analyzed how entity-exchange phenomena themselves shape extraction performance.
We model conversational entity exchange along three orthogonal axes: Initiation (how an entity becomes relevant in the dialogue), Evolution (how commitment to an entity’s value develops or changes across turns), and Articulation (how the final committed value is expressed in surface form).
We evaluate 16 large language models on 6,387 real-world customer–agent conversations spanning 12 entity types across numeric, alphanumeric, temporal, and free-text categories. Performance varies by as much as 50–60% within the same model depending solely on the underlying entity-exchange phenomena. The most severe failures occur when entity values are revised during the interaction and the model must distinguish intermediate mentions from the final committed value. Even in the absence of revision, digit-by-digit and encoded expressions remain persistent sources of error.
Error-Aware prompting improves extraction across all three axes, yielding average gains of up to 6.4% across models. Together, this work provides a structured framework for benchmarking entity extraction in real-world deployments and isolating systematic failure modes grounded in conversational structure.

Entity Exchange in the Wild: A Diagnostic Study of LLM Based Real-World Conversational Entity Extraction

Knowledge Graphs (KGs) are the backbone of reliable industrial data strategies, yet verbalizing them with Large Language Models (LLMs) often leads to unacceptable risks for high-stakes applications, such as hallucinations or omitted relations. To enforce strict semantic fidelity in KG-to-text generation, we introduce a self-supervised round-trip pipeline. The system verbalizes KG triples into text and immediately attempts to reconstruct the original graph from that text; only verbalizations that enable perfect graph recovery are retained. This creates a closed feedback loop that guarantees the generated text is semantically equivalent to the source data. Experiments confirm that our automated round-trip consistency score correlates strongly with expert judgment, effectively acting as a scalable proxy for human review. Furthermore, we show that standard LLMs can bootstrap their own KG-extraction and generation capabilities by fine-tuning on this trusted synthetic data. Our approach yields significant improvements in triple-extraction accuracy and verbalization faithfulness without relying on costly manual annotation or massive teacher models, offering a practical path to deploying trustworthy, KG-grounded AI systems.

From Graph to Text and Back: Semantic Fidelity in Automated Industrial Knowledge Graphs

We present a deployed system that automates end-to-end customer support workflows inside an enterprise Business Process Management (BPM) platform. The approach is scalable in production and reaches selective automation within two weeks for a new process, leveraging supervision already generated at scale: structured per-case UI interaction traces and low-overhead copilot feedback, where operators either accept a suggestion or provide a correction. A staged deployment pipeline trains a next UI action policy, learns a critic from copilot feedback to calibrate abstention, and executes only high-confidence steps in the background while deferring uncertain decisions to operators and resuming from the updated UI state. This setup lets one operator supervise multiple concurrent sessions and be interrupted only when the system is uncertain. The system operates on a schema-driven view of the BPM interface and includes monitoring and safe fallbacks for production. In production, it automated 45% of sessions and reduced average handling time by 39% without degrading support quality level.

Learning Selective LLM Autonomy from Copilot Feedback in Enterprise Customer Support Workflows

Mechanistic interpretability aims to reverse-engineer large language models (LLMs) into human-understandable computational circuits. However, the complexity of pretrained models often obscures the minimal mechanisms required for specific reasoning tasks. In this work, we train small, attention-only transformers from scratch on a symbolic version of the Indirect Object Identification (IOI) task, a benchmark for studying coreference-like reasoning in transformers. Surprisingly, a single-layer model with only two attention heads achieves perfect IOI accuracy, despite lacking MLPs and normalization layers. Through residual stream decomposition, spectral analysis, and embedding interventions, we find that the two heads specialize into additive and contrastive subcircuits that jointly implement IOI resolution. Furthermore, we show that a two-layer, one-head model achieves performance comparable to that of a multi-head model by composing information across layers primarily through query-key interactions. These results demonstrate that task-specific training induces highly interpretable, minimal circuits, offering a controlled testbed for probing the computational foundations of transformer reasoning.

Emergence of Minimal Circuits for Indirect Object Identification in Attention-Only Transformers

Logical fallacy detection models frequently over-flag valid reasoning due to reliance on surface-level spurious correlations, a brittleness that persists even in state-of-the-art benchmarks like CoCoLoFa despite strong in-distribution performance. This study introduces a targeted debiasing strategy using 703 LLM-generated counterfactually augmented pairs ---minimally differentiated valid and fallacious arguments matched on topic, structure, and wording---to isolate logical distinctions beyond superficial cues. Fine-tuning DeBERTa-v3-large on CoCoLoFa augmented with these pairs yields only marginal in-distribution improvement (+0.4\% F1 on the test set) but delivers substantial out-of-distribution robustness: a 58\% relative reduction in false positive rate (64\% 26.7\%) on a 300-sample balanced manual evaluation set curated from Reddit and other real-world sources. Zero-shot evaluation of recent LLMs (Llama-3.1-8B and Llama-3.3-70B) shows high performance under carefully optimized prompts (F1 90--94\%), but severe degradation under simple human-like prompts (F1 63--72\%, FPR rising to 54--74\%) and partial recovery with few-shot examples (F1 83--90\%, FPR 21--34\%). In contrast, the proposed lightweight and prompt-invariant approach achieves competitive robustness (F1 85.9\%, FPR 26.7\%) across all prompting regimes without any dependence on prompt engineering. This makes the method particularly stable for environments with unpredictable user input. The 703-pair counterfactual dataset, the fine-tuned model, and an interactive Hugging Face demo are publicly released to facilitate further research in misinformation detection and argumentation analysis.

Debiasing Logical Fallacy Detection for Real-World Robustness via Counterfactually Augmented Data

User-level ADHD-related text classification from social media is methodologically challenging because predictions must aggregate many short posts, performance can be inflated by direct diagnostic leakage, and screening-adjacent settings require calibrated probabilities rather than discrimination alone. We introduce a leakage-aware evaluation framework organized around two controlled axes: evidence budget, i.e., the number of tweets available per user, and leakage control. Within this setup, we compare document-level transformers, strong non-graph embedding-pooling baselines, and heterogeneous graph models combining semantic tweet embeddings, psycholinguistic features, and temporal structure. The main result is regime-dependent: graph aggregation is most useful when user evidence is scarce, whereas simple embedding pooling becomes highly competitive and often slightly stronger as more evidence becomes available. Overall, the main contribution is a controlled benchmarking framework and a clearer account of when graph-based aggregation is actually beneficial.

Leakage-Aware User-Level ADHD Signal Classification from Social Media: When Graph Aggregation Helps, and When It Does Not

Speaker diarization systems produce segmentation errors, such as false splits and boundary misplacements, that degrade transcript readability and downstream applications. We present CBAL (Context-Based Agentic Learning), a post-processing framework that refines segmentation boundaries in diarized scripts through targeted error correction. CBAL detects potential segmentation errors using acoustic and temporal heuristics and employs a lightweight LLM agent to reason about merge decisions, validating corrections through uncertainty-aware filtering with signal-based constraints. CBAL achieves 93.4\% accuracy across 359 applied merges and reduces segment count by 6.1\%. We demonstrate that our framework identifies and corrects high-confidence errors while maintaining 0\% degradation in terms of concatenated minimum-permutation Word Error Rate (cpWER). An ablation study confirms that each component contributes non-redundantly, demonstrating the viability of interpretable refinement frameworks that use the strengths of acoustic models and language understanding without requiring end-to-end retraining.

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Harmonizing Dense and Sparse Signals in Multi-turn RL: Dual-Horizon Credit Assignment for Industrial Sales Agents

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