Blame for README.md

---
license: cc-by-sa-4.0
task_categories: [text-generation]
language: [en, hi]
tags: [code-mixing, hinglish, reasoning, evaluation, beam]
pretty_name: HinglishBench
---

# HinglishBench: a parallel English / Hinglish reasoning evaluation for open LLMs

**What happens when a 501B reasoning model meets the way a billion people actually talk?**
Beam-1.0-501B-A23B is "developed predominantly for English" (its own model card). Hinglish - the
romanized Hindi-English code-mix used daily across India - is not English. HinglishBench measures the gap.

This is a **parallel code-mixed (English/Hinglish) reasoning evaluation for frontier open models**:
every item exists in two language versions of the *same* problem, so accuracy and
cost differences are attributable to language, not difficulty. (GLUECoS, Khanuja et al. 2020, covered
code-mixed *encoder* tasks; we evaluate generative reasoning.)

## Headline results (GSM8K-Hinglish, 82 parallel pairs, zero-shot, single run)

| Model | English | Hinglish | Gap (pts) | Median token tax (HI/EN) |
|---|---|---|---|---|
| **Beam-1.0-501B-A23B** (reasoning_effort=low) | 96.3% | **95.1%** | -1.2 | **1.47x** |
| Gemma-3-27B-it | 91.5% | 93.9% | +2.4 | 0.96x |
| Qwen3-32B | 90.2% | 82.9% | **-7.3** | 1.00x |

1. **Beam scores highest on Hinglish of the three tested** (95.1%) despite English-predominant training -
   one item ahead of Gemma (93.9%) - but a Hinglish answer costs it a median ~1.5x completion tokens. On these
   runs, Beam's Hinglish tax shows up as serving cost rather than accuracy.
2. **The tax is not universal.** Gemma-3-27B shows no Hinglish penalty in these runs (accuracy slightly
   *above* English, no token cost). Qwen3-32B scores 7.3 accuracy points lower on Hinglish.
3. **The two prompt variants we tested don't remove the tax.** A "think in English" system prompt on 30 Hinglish items changed accuracy
   by 0 points and *increased* tokens by 9.9%. `reasoning_effort=medium` does not buy it back either
   (observed tax 1.39x vs 1.47x; one sample per cell, so we do not estimate run-to-run variance. BeamDial
   likewise reports effort levels tying on standard math).
4. A second controlled set (100 author-built parallel pairs in easy/medium/hard tiers, Beam only) replicates
   the tax: 98.0% = 98.0% accuracy, **1.58x** median token tax.
5. **Reverse-direction replication.** A third set (309 parallel pairs: Beam-authored Hinglish-first problems
   translated to English) shows Beam's tax again - **1.30x** - with accuracy saturated (99.4% EN / 99.7% HI).
   Gemma 0.99x and Qwen 0.94x again show no tax. So the tax is **not limited to English-first GSM8K items
   translated into Hinglish**; the reverse set is itself translated, Beam-authored and easier, so it bounds
   but does not eliminate translation/selection explanations (see Limits).

## What's in this dataset

- `gsm8k_hinglish.jsonl` - 82 parallel pairs: GSM8K test items (English) + romanized-Hinglish translations, with gold answers.
- `tiered_100.jsonl` - 100 author-built parallel pairs in easy/medium/hard difficulty tiers, with gold answers.
- `hinglish_reasoning_beam.jsonl` - 326 original Hinglish-first math problems authored by Beam (romanized,
  Indian context, 12 topics x easy/medium/hard), each with a numeric gold answer verified by self-consistency.
- `beamgen_rejected.jsonl` - the 7 generation/solve mismatches and automated-check rejects, with reasons.
- `beamgen_parallel.jsonl` - 309 parallel EN/Hinglish pairs: the reverse-direction set (Hinglish-first items
  + their English translations), used for headline point 5.
- `beamgen_parallel_excluded.txt` - the 17 translation-check exclusions from that set, with the same
  numeric-preservation rule as `excluded_items.txt`.
- `predictions/` - per-item raw outputs, parsed predictions, correctness and token usage for every
  (model, language, item) cell, including the reasoning_effort=medium, mitigation, and reverse-direction runs.

## Methodology

- **Items:** 100 GSM8K test items, uniform random sample (seed 42). Translated to natural spoken-style
  romanized Hinglish **by Beam itself** (reasoning_effort=low) - we verified every translation with an
  automated numeric-token preservation check and **excluded 18 items** that failed it (IDs listed in
  `excluded_items.txt`), leaving 82 pairs. During preparation, 5 kept translations were additionally read end-to-end and found
  faithful (spot-check performed by the author's AI assistant, not an independent human reviewer).
- **Prompting (all models, identical):** "Solve the problem. You may think briefly, then end your reply with a
  final line containing ONLY the numeric answer." Zero-shot, no few-shot. (An earlier answer-only prompt was
  scrapped: it unfairly suppresses non-reasoning models, dropping Gemma to 25%.)
- **Grading:** numeric exact match on the final line (fallback: last number in output).
- **Inference:** HF Inference Providers. Beam via Baseten at `reasoning_effort=low` (plus a `medium` run);
  Qwen3-32B with thinking disabled; Gemma-3-27B-it default. Single sample per cell.
- **Token tax:** per-item ratio of completion tokens (Hinglish / English), reported as the median over pairs.
- **Beam-authored Hinglish set:** Beam generated 340 original romanized-Hinglish word problems (12 everyday
  topics, 3 difficulty tiers, reasoning_effort=low). 333 survived automated checks (Latin script only, >=2
  Hinglish marker words, length bounds) plus near-duplicate filtering. A fresh Beam instance then solved each
  item independently; the 326 items where the solver's answer matched the authored key (numeric exact match)
  were kept; the 7 disagreements went to `beamgen_rejected.jsonl`. A random 30-item sample of kept items was
  re-solved and read during preparation (performed by the author's AI assistant, not an independent human
  reviewer): 30/30 keys correct, Hinglish natural.
- **Reverse-direction set:** the 326 verified Hinglish-first items were translated to natural English by Beam
  (reasoning_effort=low) under the same automated numeric-preservation check (with comma/word-number
  normalization). 17 items failed (13 empty translations, 2 truncations, 2 word-number renderings; IDs in
  `beamgen_parallel_excluded.txt`) and were excluded, leaving 309 pairs. Same prompt, grading, and
  paired-median token-tax definition as the GSM8K-Hinglish runs; all three models ran under the identical
  fair prompt used for the headline table.
- **Cost:** total inference spend for the entire project (all pilots, calibration, translations, every
  reported run, and this reverse-direction run: ~3,700 generations) measured **$0.95** against HF
  billing on Oct 23, 2026. Per-call token usage is logged in the prediction files.

## Limits (read before citing)

- Single run per cell, zero-shot; n=82. We report observed differences without claiming statistical
  significance (e.g. Beam low vs medium effort differs by about one item).
- Romanized Hinglish only - no Devanagari, no other code-mixed pairs.
- Hinglish translations are model-generated (Beam) and automatically verified, not human-certified gold.
- GSM8K is English-US-context word math; the translation preserves context (dollars stay dollars).
- One provider per model, one temperature (default), one prompt format.
- Author-built tiered items are easier than GSM8K and share one author's style.
- Beam-authored items were both written and verified by Beam (self-consistency, not human gold); a 30-item
  sample re-checked by the author's AI assistant passed, but residual key errors at the ~1-2% level are
  plausible.
- The reverse-direction set is still translated (Hinglish to English), Beam-authored, and easier than GSM8K:
  accuracy saturates there (Beam 99%+), so it evidences the token tax replicating beyond English-first
  translated items, not a full refutation of translation artifacts; Beam-authored topic/style selection and
  the 17 excluded items can bias it.

## Why this matters

Serving costs scale with output tokens. If Hinglish answers cost ~1.5x English answers at equal accuracy,
serving India's code-mixed majority on Beam costs ~1.5x more per answer - invisible in English-only evals.
The fix is not prompting (we tested); candidate fixes live in tokenization and training data. Beam's own
translations passed 82% automated verification, which suggests open models can already bootstrap
code-mixed data.

## Citation

```
@misc{hinglishbench2026,
  title  = {HinglishBench: a parallel English/Hinglish reasoning evaluation for open LLMs},
  author = {Kharya, Sanskar},
  year   = {2026},
  howpublished = {Hugging Face dataset, Build with Beam community challenge entry}
}
```

GLUECoS: Khanuja et al., "GLUECoS: An Evaluation Benchmark for Code-Switched NLP", ACL 2020.
GSM8K: Cobbe et al., "Training Verifiers to Solve Math Word Problems", 2021 (MIT license).