| --- |
| 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). |
| |
