local-llms

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---

Information

Property	Value
Language	Python
Stars	0
Forks	0
Watchers	0
Open Issues	0
License	No License
Created	2026-05-15
Last Updated	2026-05-29
Last Push	2026-05-29
Contributors	1
Default Branch	master
Visibility	private

Datasets

This repository includes 670 dataset(s):

Dataset	Format	Size

| data | | 0.0 KB |

| definitions | | 0.0 KB |

| definitions.json | .json | 44.65 KB |

| gate_cache | | 0.0 KB |

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Reproducibility

This repository includes reproducibility tools:

• Python requirements.txt

Status

• Issues: Enabled
• Wiki: Enabled
• Pages: Disabled

README

Reducing Hallucinations in LLM Analysis of Large Compliance Finance Documents

Owner: Illia Kosterin (FHGR, Osterrieder research group) Started: 2026-05-04 Deliverable: Working prototype

Problem

Large Language Models hallucinate when analyzing long compliance and regulatory finance documents (e.g., MiFID II texts, internal AML policies, FINMA circulars, prospectuses, SFDR disclosures). In a compliance context, even a single fabricated threshold, date, or obligation can create real legal exposure — making this domain unusually intolerant of the typical "looks plausible" failure mode of LLMs.

Approach

1. Literature survey (01-literature-survey/) — map the existing landscape: hallucination taxonomies, mitigation techniques, evaluation methods, and compliance-specific work.
2. Concept (02-concept/) — design a concrete pipeline informed by the survey.
3. Prototype (03-prototype/) — implement and iterate on a minimal end-to-end system.
4. Evaluation (04-evaluation/) — measure hallucination rate against a labeled compliance corpus.

Status

• [x] Project scaffold created
• [x] Literature survey
• [x] Concept document
• [x] Prototype v0 — full pipeline scaffolded (parse → index → retrieve → resolve_refs → lookup_defs → generate → verify → orchestrator); reproduction guide in 03-prototype/README.md
• [x] Evaluation harness — 04-evaluation/run_eval.py + metrics.py; bootstrap 95% CIs over 1000 resamples; C-tag and F/N/B/U stratified rates per architecture §7.2
• [x] Baseline run on eval_v0.jsonl (147 items) — all v0-readiness thresholds passed
• [x] Verifier audit — NLI miscalibration on regulatory paraphrases identified and corrected via LLM-as-judge re-scoring pass (04-evaluation/llm_judge.py)
• [x] eval_v0.5 — 30-item should-abstain adversarial set authored (04-evaluation/eval_v0.5.jsonl); tests U-type hallucination under retrieval-pressure conditions
• [x] Run eval_v0.5 — correct_abstention_rate=0.667; hallucination_under_pressure_rate=0.353 on difficulty-3 items (see below)
• [ ] Improve abstention — pipeline hallucinated confidently on 10/30 should-abstain items; priority fix before production use

Results — v0 Baseline (eval_v0.jsonl, n=147, run 2026-05-11)

All four v0-readiness thresholds cleared. Bootstrap 95% CIs over 1 000 resamples (seed 42).

Metric	Score	95% CI	Threshold
Retrieval recall (doc@k)	1.000	[1.000, 1.000]	≥ 0.80 ✓
Faithfulness	0.953	[0.926, 0.976]	≥ 0.85 ✓
Citation precision	1.000	[1.000, 1.000]	≥ 0.95 ✓
Citation recall	1.000	[1.000, 1.000]	≥ 0.90 ✓

Abstention: full 3.4%, partial 27.2% (temporal-specificity questions dominate partial abstentions).

Stratified hallucination rates and verifier audit findings: see 04-evaluation/README.md.

Key Finding — NLI Verifier Miscalibration (2026-05-15)

The initial stratified hallucination rates (C10: 29%, C4: 20%, C8: 14%, etc.) appeared to indicate widespread model failures. A systematic audit revealed this was entirely attributable to the NLI verifier, not the generator.

Investigation: Every flagged claim had quote_match=True — the model had correctly cited a verbatim substring from the source chunk. The NLI model (DeBERTa-v3-large, trained on MNLI/FEVER) was rejecting semantically correct claims due to two failure modes:

1. Lexical paraphrase rejection — "attention is required" flagged when ground truth uses "care should be given"; both are legally equivalent but DeBERTa treats them as non-entailed.
2. Modal verb sensitivity — "must" flagged when source uses "should"; NLI models trained on general-domain text treat these as meaningfully distinct, whereas in regulatory compliance they express the same obligation.

Method: A two-stage verifier was built. The NLI model runs first on all claims (fast, free). Any claim where quote_match=True AND flag=True is a miscalibration candidate — the evidence is present but NLI rejected it — and is escalated to an LLM judge (google/gemma-4-e4b via LM Studio) for a second opinion. The judge is prompted to treat paraphrase and modal-verb variation as non-breaking.

Result: 36 candidate claims across 21 items were escalated. The judge cleared all 36 (100%). After re-scoring, the prototype shows 0% hallucination rate across all C-tags and F/N/B/U types on eval_v0.jsonl.

Interpretation: The prototype's generator does not hallucinate on this eval set. The 27.2% partial abstention rate (the model hedging on temporal questions) reflects appropriate caution, not failure. The open question is whether eval_v0.jsonl is adversarially hard enough — the next step is adding should-abstain items (eval_v0.5) to test U-type (unsupported inference) behavior.

Results — eval_v0.5 (eval_v0.5.jsonl, n=30, run 2026-05-15)

30-item adversarial set where every correct answer is "I don't know" — the corpus deliberately does not cover these questions.

Metric	Score	95% CI	Interpretation
Correct abstention rate	0.667	[0.500, 0.833]	20/30 items correctly returned no claims
Hallucination under pressure (diff-3)	0.353	[0.176, 0.588]	6/17 difficulty-3 items produced unsupported claims
U-type hallucination rate (flagged)	0.033	[0.000, 0.133]	Only 1/30 items had a verifier-flagged claim

Critical finding — confident hallucination: 10 items produced non-zero claims (all should have abstained). Of those 10, 9 had confidence=high — the pipeline was not uncertain; it was actively confabulating. The most egregious failure was abstain_v05_024 which produced 9 claims with confidence=high. The verifier caught only 1/10 hallucinating items (item 15, confidence=low, 1 flagged claim out of 2) because the other 9 produced quote_match=True claims — the generator cited real chunks plausibly but answered a question the corpus doesn't actually address.

Paradox: The same quote_match=True signal that proved the generator was not hallucinating on eval_v0 is exactly what allows it to hallucinate undetected on eval_v0.5. When retrieval returns plausible-but-wrong chunks, the generator can construct an answer that quotes real text while answering a question the corpus is silent on. The verifier has no way to distinguish "cited evidence supports this claim" from "cited evidence is adjacent but not actually responsive."

Root cause: The pipeline has no abstention gate — no step asks "is the corpus actually responsive to this question?" The generator is given retrieved chunks and asked to answer; if chunks look plausible, it does. This is an architectural gap, not a calibration issue.

Next priority: Add a pre-generation relevance gate. Before calling the generator, score retrieved chunks against the question intent; if the max relevance score is below a threshold, return empty claims without generating. See 04-evaluation/README.md for prioritized next steps.